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Dias Canedo, Edna, and Bruno Cordeiro Mendes. "Software Requirements Classification Using Machine Learning Algorithms." Entropy 22, no. 9 (2020): 1057. http://dx.doi.org/10.3390/e22091057.
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The correct classification of requirements has become an essential task within software engineering. This study shows a comparison among the text feature extraction techniques, and machine learning algorithms to the problem of requirements engineer classification to answer the two major questions “Which works best (Bag of Words (BoW) vs. Term Frequency–Inverse Document Frequency (TF-IDF) vs. Chi Squared (CHI2)) for classifying Software Requirements into Functional Requirements (FR) and Non-Functional Requirements (NF), and the sub-classes of Non-Functional Requirements?” and “Which Machine Learning Algorithm provides the best performance for the requirements classification task?”. The data used to perform the research was the PROMISE_exp, a recently made dataset that expands the already known PROMISE repository, a repository that contains labeled software requirements. All the documents from the database were cleaned with a set of normalization steps and the two feature extractions, and feature selection techniques used were BoW, TF-IDF and CHI2 respectively. The algorithms used for classification were Logist Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB) and k-Nearest Neighbors (kNN). The novelty of our work is the data used to perform the experiment, the details of the steps used to reproduce the classification, and the comparison between BoW, TF-IDF and CHI2 for this repository not having been covered by other studies. This work will serve as a reference for the software engineering community and will help other researchers to understand the requirement classification process. We noticed that the use of TF-IDF followed by the use of LR had a better classification result to differentiate requirements, with an F-measure of 0.91 in binary classification (tying with SVM in that case), 0.74 in NF classification and 0.78 in general classification. As future work we intend to compare more algorithms and new forms to improve the precision of our models.
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Wastu, Klaus Rajendra. "COMPARISON BAGGING AND SUPPORT VECTOR MACHINE FOR CLASSIFICATION SOFTWARE REQUIREMENT." Proxies : Jurnal Informatika 8, no. 1 (2024): 22–33. http://dx.doi.org/10.24167/proxies.v8i1.12475.
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Software Requirements Specifications is a document that describes the requirements that occur in the development of a software system. The category of requirements is defined in two types: Functional Requirements (FR) and Non-Functional Requirements (NFR). Software Requirements Engineering is critical in successfully designing a piece of software. Many studies have examined the classification of software requirements using machine learning, but none have compared bagging algorithms with Support Vector Machine (SVM). This study compares text feature extraction techniques with machine learning algorithms Bagging and Support Vector Machine to solve the Software Requirement Classification problem. Using vectorization techniques from word2vec: Continuous Bag of Words and Skip-gram can help produce the best model performance for Bagging and SVM models. In this study, the data used is expansion data from the PROMISE repository, namely PROMISE_exp, the repository is a collection of software requirements data that has been labeled. To measure performance, this study uses an evaluation matrix, namely precision, recall and f1-score. As a result, the two models that have been trained using the Continuous Bag of Words and skip-gram vectorization techniques will be compared to determine the more optimal model for classifying software requirements from the promise_exp repository.
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Baskoro, Fajar, Rasi Aziizah Andrahsmara, Brian Rizqi Paradisiaca Darnoto, and Yoga Ari Tofan. "A Systematic Comparison of Software Requirements Classification." IPTEK The Journal for Technology and Science 32, no. 3 (2021): 184. http://dx.doi.org/10.12962/j20882033.v32i3.13005.
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Sheet, Sama Emad, and Ibrahim Ahmed Saleh. "Software Requirement Specifications Using Intelligent Technical: Literature Review." International Research Journal of Innovations in Engineering and Technology 08, no. 08 (2024): 273–78. http://dx.doi.org/10.47001/irjiet/2024.808032.
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Software requirement is become more important in recent because the development which witness in projects, badly executed requirements engineering steps can result in bad quality software and more cost for expensive maintenance. Manual classification of requirements is difficult, time-consuming, and expensive, especially in large projects and is written as a Software Requirements Specification (SRS) document. For this reason, automating software requirements classification helps in obtaining higher accuracy and saving time and effort. Most of researcher applied Intelligence techniques algorithms to avoid erroneous requirements and human intervention, as well as analyze, classify, and priority of requirements. In this paper illustrated modern of artificial techniques algorithm to classify RT approaches. It is surveyed that existing techniques like machine learning algorithms such as K-Nearest Neighbor (K-NN), decision tree (DT),.. etc. Many other technical how ensemble learning and deep learning algorithm results in classification of RF. Researchers have proposed automated techniques to classify functional and non-functional requirements using several machine learning (ML) algorithms with a combination of different vector techniques. However, using the best method in classifying functional and non-functional requirements still needs clarification, and through many studies and research by researchers.
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Abdulmunim Abdulmajeed Althanoon, Ashraf, and Younis S. Younis. "Supporting Classification of Software Requirements system Using Intelligent Technologies Algorithms." Technium: Romanian Journal of Applied Sciences and Technology 3, no. 11 (2021): 32–39. http://dx.doi.org/10.47577/technium.v3i11.5417.
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The important first stage in the life cycle of a program is gathering and analysing requirements for creating or developing a system. The classification of program needs is a crucial step that will be used later in the design and implementation phases. The classification process may be done manually, which takes a lot of time, effort, and money, or it can be done automatically using intelligent approaches, which takes a lot less time, effort, and money. Building a system that supports the needs classification process automatically is a crucial part of software development. The goal of this research is to look into the many automatic classification approaches that are currently available. To assist researchers and software developers in selecting the suitable requirement categorization approach, those requirements were divided into functional and non-functional requirements. since natural language is full of ambiguity and is not well defined, and has no regular structure, it is considered somewhat variable. This paper presents machine requirement classification where system development requirements are categorized into functional and non-functional requirements by using two machine learning approaches. During this research paper, MATLAB 2020a was used, as well as the study's results indicate When applying Multinomial Naive Bayes technology, the model achieves the highest accuracy of 95.55 %,93.09 % sensitivity, and 96.48 % precision, However, when using Logist Regression, the suggested model has a classification accuracy of 91.23 %,91.54 % sensitivity, and 94.32 % precision.
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Rahman, Abdur, Abu Nayem, and Saeed Siddik. "Non-Functional Requirements Classification Using Machine Learning Algorithms." International Journal of Intelligent Systems and Applications 15, no. 3 (2023): 56–69. http://dx.doi.org/10.5815/ijisa.2023.03.05.
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Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.
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Mahalakshmi, K., Udayakumar Allimuthu, L. Jayakumar, and Ankur Dumka. "A Timeline Optimization Approach of Green Requirement Engineering Framework for Efficient Categorized Natural Language Documents in Non-Functional Requirements." International Journal of Business Analytics 8, no. 1 (2021): 21–37. http://dx.doi.org/10.4018/ijban.2021010102.
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The system's functional requirements (FR) and non-functional requirements (NFR) are derived from the software requirements specification (SRS). The requirement specification is challenging in classification process of FR and NFR requirements. To overcome these issues, the work contains various significant contributions towards SRS, such as green requirements engineering (GRE), to achieve the natural language processing, requirement specification, extraction, classification, requirement specification, feature selection, and testing the quality attributes improvement of NFRs. In addition to this, the test pad-based quality study to determine accuracy, quality, and condition providence to the classification of non-functional requirements (NFR) is also carried out. The resulted classification accuracy was implemented in the MATLAB R2014; the resulted graphical record shows the efficient non-functional requirements (NFR) classification with green requirements engineering (GRE) framework.
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زاید, مصطفى عادل. "Automatic Software Requirements Classification: A Systematic Literature Review." النشرة المعلوماتیة فی الحاسبات والمعلومات 3, no. 1 (2021): 29–37. http://dx.doi.org/10.21608/fcihib.2021.56466.1008.
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Rashme, Tamanna Yesmin. "Mapping Software Requirements: An Overview of Classification Strategies." International Journal of Applied Information Systems 12, no. 45 (2024): 1–6. http://dx.doi.org/10.5120/ijais2024451976.
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Salleh, Amran, Mar Yah Said, Mohd Hafeez Osman, and Sa’adah Hassan. "A Review on Classifying and Prioritizing User Review-Based Software Requirements." JOIV : International Journal on Informatics Visualization 8, no. 3-2 (2024): 1651. https://doi.org/10.62527/joiv.8.3-2.3450.
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User reviews are a valuable source of feedback for software developers, as they contain user requirements, opinions, and expectations regarding app usage, including dislikes, feature requests, and reporting bugs. However, extracting and analyzing user requirements from user reviews is ineffective due to the large volume, unstructured nature, and varying quality of the reviews. Therefore, further research is not just necessary but crucial to effectively explore methods to gather informative and meaningful user feedback. This study aims to investigate, analyze, and summarize the methods of requirement classification and prioritization techniques derived from user reviews. This review revealed that leveraging opinion mining, sentiment analysis, natural language processing, or any stacking technique can significantly enhance the extraction and classification processes. Additionally, an updated matrix taxonomy has been developed based on a combination of definitions from various studies to classify user reviews into four main categories: information seeking, feature request, problem discovery, and information giving. Furthermore, we identified Naive Bayes, SVM, and Neural Networks algorithms as dependable and suitable for requirement classification and prioritization tasks. The study also introduced a new 4-tuple pattern for efficient requirement prioritization, which included elicitation technique, requirement classification, additional factors, and higher range priority value. This study highlights the need for better tools to handle complex user reviews. Investigating the potential of emerging machine learning models and algorithms to improve classification and prioritization accuracy is crucial. Additionally, further research should explore automated classification to enhance efficiency.
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Pérez-Verdejo, J. Manuel, Á. J. Sánchez-García, J. O. Ocharán-Hernández, E. Mezura-Montes, and K. Cortés-Verdín. "Requirements and GitHub Issues: An Automated Approach for Quality Requirements Classification." Programming and Computer Software 47, no. 8 (2021): 704–21. http://dx.doi.org/10.1134/s0361768821080193.
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Petrillo, Luca, Fabio Martinelli, Antonella Santone, and Francesco Mercaldo. "Explainable Security Requirements Classification Through Transformer Models." Future Internet 17, no. 1 (2025): 15. https://doi.org/10.3390/fi17010015.
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Security and non-security requirements are two critical issues in software development. Classifying requirements is crucial as it aids in recalling security needs during the early stages of development, ultimately leading to enhanced security in the final software solution. However, it remains a challenging task to classify requirements into security and non-security categories automatically. In this work, we propose a novel method for automatically classifying software requirements using transformer models to address these challenges. In this work, we fine-tuned four pre-trained transformers using four datasets (the original one and the three augmented versions). In addition, we employ few-shot learning techniques by leveraging transfer learning models, explicitly utilizing pre-trained architectures. The study demonstrates that these models can effectively classify security requirements with reasonable accuracy, precision, recall, and F1-score, demonstrating that the fine-tuning and SetFit can help smaller models generalize, making them suitable for enhancing security processes in the Software Development Cycle. Finally, we introduced the explainability of fine-tuned models to elucidate how each model extracts and interprets critical information from input sequences through attention visualization heatmaps.
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Jayatilleke, Shalinka, Richard Lai, and Karl Reed. "Managing software requirements changes through change specification and classification." Computer Science and Information Systems 15, no. 2 (2018): 321–46. http://dx.doi.org/10.2298/csis161130041j.
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Software requirements changes are often inevitable due to the changing nature of running a business and operating the Information Technology (IT) system which supports the business. As such, managing software requirements changes is an important part of software development. Past research has shown that failing to manage software requirements changes effectively is a main contributor to project failure. One of the difficulties in managing requirements changes is the lack of effective methods for communicating changes from the business to the IT professionals. In this paper, we present an approach to managing requirements change by improving the change communication and elicitation through a method of change specification and a method of classification. Change specification provides a way such that communication ambiguities can be avoided between business and IT staff. The change classification mechanism identifies the type of the changes to be made and preliminary identification of the actions to be taken. We illustrate the usefulness of the methods by applying them to a case study of course management system.
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Hoy, Zoe, and Mark Xu. "Agile Software Requirements Engineering Challenges-Solutions—A Conceptual Framework from Systematic Literature Review." Information 14, no. 6 (2023): 322. http://dx.doi.org/10.3390/info14060322.
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Agile software requirements engineering processes enable quick responses to reflect changes in the client’s software requirements. However, there are challenges associated with agile requirements engineering processes, which hinder fast, sustainable software development. Research addressing the challenges with available solutions is patchy, diverse and inclusive. In this study, we use a systematic literature review coupled with thematic classification and gap mapping analysis to examine extant solutions against challenges; the typologies/classifications of challenges faced with agile software development in general and specifically in requirements engineering and how the solutions address the challenges. Our study covers the period from 2009 to 2023. Scopus—the largest database for credible academic publications was searched. Using the exclusion criteria to filter the articles, a total of 78 valid papers were selected and reviewed. Following our investigation, we develop a framework that takes a three-dimensional view of agile requirements engineering solutions and suggest an orchestrated approach balancing the focus between the business context, project management and agile techniques. This study contributes to the theoretical frontier of agile software requirement engineering approaches and guidelines for practice.
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Mughal, Muhammad Hussain, and Zubair Ahmed Shaikh. "Software Atom: An approach towards software components structuring to improve reusability." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 2 (2017): 66. http://dx.doi.org/10.30537/sjcms.v1i2.31.
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Diversity of application domain compelled to design sustainable classification scheme for significantly amassing software repository. The atomic reusable software components are articulated to improve the software component reusability in volatile industry. Numerous approaches of software classification have been proposed over past decades. Each approach has some limitations related to coupling and cohesion. In this paper, we proposed a novel approach by constituting the software based on radical functionalities to improve software reusability. We analyze the element's semantics in Periodic Table used in chemistry to design our classification approach, and present this approach using tree-based classification to curtail software repository search space complexity and further refined based on semantic search techniques. We developed a Global unique Identifier (GUID) for indexing the functions and related components. We have exploited the correlation between chemistry element and software elements to simulate one to one mapping between them. Our approach is inspired from sustainability chemical periodic table. We have proposed software periodic table (SPT) representing atomic software components extracted from real application software. Based on SPT classified repository tree parsing & extraction to enable the user to program their software by customizing the ingredients of software requirements. The classified repository of software ingredients assist user to exploits their requirements to software engineer and enable requirement engineer to develop a rapid large-scale prototype with great essence. Furthermore, we would predict the usability of the categorized repository based on feedback of users. The continuous evolution of that proposed repository will be fine-tuned based on utilization and SPT would be gradually optimized by ant colony optimization techniques. Succinctly would provoke automating the software development process.
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Mahmoud, Mohammad. "Software Requirements Classification using Natural Language Processing and SVD." International Journal of Computer Applications 164, no. 1 (2017): 7–12. http://dx.doi.org/10.5120/ijca2017913555.
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Davey, Bill, and Kevin R. Parker. "Requirements Elicitation Problems: A Literature Analysis." Issues in Informing Science and Information Technology 12 (2015): 071–82. http://dx.doi.org/10.28945/2211.
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Requirements elicitation is the process through which analysts determine the software requirements of stakeholders. Requirements elicitation is seldom well done, and an inaccurate or incomplete understanding of user requirements has led to the downfall of many software projects. This paper proposes a classification of problem types that occur in requirements elicitation. The classification has been derived from a literature analysis. Papers reporting on techniques for improving requirements elicitation practice were examined for the problem the technique was designed to address. In each classification the most recent or prominent techniques for ameliorating the problems are presented. The classification allows the requirements engineer to be sensitive to problems as they arise and the educator to structure delivery of requirements elicitation training.
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Saleem, Muhammad Bin, Nosheen Qamar, Rehana Danial, Kinza Sardar, Maham Noor, and Uzma Omer. "Analyzing the Impact of Machine Learning Algorithms on Software Requirements Classification." VFAST Transactions on Software Engineering 13, no. 1 (2025): 88–98. https://doi.org/10.21015/vtse.v13i1.2051.
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Along with the rapid growth of the world, the demand for efficient and successful software has increased swiftly. Any software has many steps for developing software and the most important step is software requirements engineering. Requirements classification can be applied manually, which requires great effort, time, and cost and the accuracy may vary. Many previous studies utilized machine learning algorithms to automate the classification process but traditional classification algorithms often require a large amount of labeled data, which can be expensive and time-consuming to collect. Few-Shot Learning (FSL) excels in situations with limited data, making it a promising alternative. This paper investigates the potential of applying Few-Shot Learning (FSL) algorithms for classifying software requirements. This study explores three prominent FSL algorithms: Prototypical Networks, Matching Networks, and Model-Agnostic Meta-Learning (MAML). These algorithms are evaluated on their ability to classify software requirements using a publicly available dataset. The results demonstrate that Prototypical Networks outperforms Matching Networks and MAML in this specific application. Matching Networks, designed for visual similarity tasks, struggle with textual data. Prototypical Networks achieve a remarkable accuracy of 82 percent, suggesting their effectiveness in learning class representations from a small number of samples. MAML also shows promising results with an accuracy of 76.9 percent. While acknowledging limitations in data pre-processing, the study concludes that FSL holds significant potential for efficient and cost-effective software requirement classification, particularly when dealing with limited labeled data.
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Sonali Idate. "Performance analysis of Machine Learning Algorithms to classify Software Requirements." Journal of Electrical Systems 20, no. 2 (2024): 1588–99. http://dx.doi.org/10.52783/jes.1464.
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Building a software system requires a clear understanding of its purpose and its operational characteristics. Functional requirements establish the system's purpose, while non-functional requirements define its operational performance aspects. It is essential to identify and classify requirements accurately to develop reliable software. In this research paper, we aim to classify functional and non-functional software requirements using different Machine Learning algorithms and techniques. We utilized four popular classification models, including Logistic Regression, Support Vector Machines, Decision Tree, and Random Forest Multi-layer Perceptron Neural Network to classify the requirements. To enhance the accuracy of the classification, we also applied a technique based on cosine similarity to verify if the custom string provided as input is related to software requirements. The addition of cosine similarity improved the accuracy of classification and reduced the misclassification of non-requirements.
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Fahmi, Achmad An'im, and Daniel Siahaan. "Algorithms Comparison for Non-Requirements Classification using the Semantic Feature of Software Requirement Statements." IPTEK The Journal for Technology and Science 31, no. 3 (2021): 343. http://dx.doi.org/10.12962/j20882033.v31i3.7606.
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Jogannagari, Malla Reddy* S.V.A.V. Prasad Sambasiva Rao Baragada. "REQUIREMENT ENGINEERING : AN APPROACH TO QUALITY SOFTWARE DEVELOPMENT." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 177–84. https://doi.org/10.5281/zenodo.192559.
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Requirement Engineering is the crucial phase of software development life cycle. The activity of requirement engineering is contract between the client and developer. The gathering of complete and consistent requirements can motive the quality of software product and can satisfy the user’s needs. The requirement engineering is a complex exercise that consider the product requirement demands from the number of viewpoints , roles and responsibilities. The successful systematic implementation of requirement engineering process can have good impact on the quality of software product.. In this research paper, we highlights the role of requirement engineering and its activities in the development of quality software product.
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Khanneh, Shada, and Vaibhav Anu. "Security Requirements Prioritization Techniques: A Survey and Classification Framework." Software 1, no. 4 (2022): 450–72. http://dx.doi.org/10.3390/software1040019.
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Security requirements Engineering (SRE) is an activity conducted during the early stage of the SDLC. SRE involves eliciting, analyzing, and documenting security requirements. Thorough SRE can help software engineers incorporate countermeasures against malicious attacks into the software’s source code itself. Even though all security requirements are considered relevant, implementing all security mechanisms that protect against every possible threat is not feasible. Security requirements must compete not only with time and budget, but also with the constraints they inflect on a software’s availability, features, and functionalities. Thus, the process of security requirements prioritization becomes an integral task in the discipline of risk-analysis and trade-off-analysis. A sound prioritization technique provides guidance for software engineers to make educated decisions on which security requirements are of topmost importance. Even though previous research has proposed various security requirement prioritization techniques, none of the existing research efforts have provided a detailed survey and comparative analysis of existing techniques. This paper uses a literature survey approach to first define security requirements engineering. Next, we identify the state-of-the-art techniques that can be adopted to impose a well-established prioritization criterion for security requirements. Our survey identified, summarized, and compared seven (7) security requirements prioritization approaches proposed in the literature.
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van Remmen, Judith Sophie, Dennis Horber, Adriana Lungu, et al. "NATURAL LANGUAGE PROCESSING IN REQUIREMENTS ENGINEERING AND ITS CHALLENGES FOR REQUIREMENTS MODELLING IN THE ENGINEERING DESIGN DOMAIN." Proceedings of the Design Society 3 (June 19, 2023): 2765–74. http://dx.doi.org/10.1017/pds.2023.277.
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AbstractRequirements represent a central element in product development. The large number of requirements inevitably results in an increased susceptibility to errors, an expenditure of time and development costs. The associated problems motivate the application of Artificial Intelligence in the form of Natural Language Processing (NLP). In Requirements Engineering one main task is the classification of requirements which serves as the input in architectural models e.g. in SysML. In mechanical engineering there is still little overview regarding the interface between requirements classification and modelling. This paper provides an overview of the requirement classes and entities used in the literature and analyses their utilisation in modelling. Existing requirements classes usually do not offer the flexibility to be transferred to other domains. However, basic structures can be adopted from those classifications. This enables a clear assignment of existing classes to object classes in modelling. Resulting from the conducted literature study the observed predominant focus of research on the software industry requires an extension of the existing requirement classes and entities to enable further use and transfer to mechanical engineering.
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Гордеев, Александр Александрович. "МОДЕЛЬ КАЧЕСТВА ОТДЕЛЬНОГО ТРЕБОВАНИЯ ПРОГРАММНОГО ОБЕСПЕЧЕНИЯ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 2 (26 квітня 2020): 48–58. http://dx.doi.org/10.32620/reks.2020.2.04.
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The basis of the specification for software development is the requirements profile, which takes into account functionality, features, limitations, risks, etc. of future software. The requirements profile is a product of the profiling process and is a taxonomic structure that links together many of the requirements for the software being developed. An indivisible unit of the requirements profile is a separate software requirement. Formally, the software requirement is a set of related requirements, but it is a more complex object, the quality of the software as a whole depends on its quality. The implementation of insufficient quality requirements in software entails resource losses. Existing works related to this issue do not fully propose the presentation of the quality model of a particular requirement. The purpose of this article is to develop a quality model of an individual software requirement. The object of research is the software requirement. The article is devoted to the development of a quality model of an individual software requirement. The idea of developing the designated model came about after analyzing the following standards: ISO / IEC / IEEE 29148: 2011 (E), ISO / IEC / IEEE 29148: 2018 (E) and ISO / IEC 25012: 2008. The provisions that are presented in the designated standards and formed the basis of this article. It considers the requirement as a separate, unrelated element of the software requirements profile. The requirement is represented in the form of elements of facet-hierarchical structures and consists of a semantic classification attribute and a semantic taxon. A five-component model of the quality of an individual software requirement is proposed, it includes structure, properties, attributes, syntax, and semantics requirements. The combination of such elements in one model allows us to formally describe the quality of an individual software requirement. As a result, this article proposes a formal description and presentation of the quality model of an individual software requirement.
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Et.al, Osman, M. H. "Ambi Detect: An Ambiguous Software Requirements Specification Detection Tool." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 2023–28. http://dx.doi.org/10.17762/turcomat.v12i3.1066.
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Software Requirements Specification (SRS) is considered a highly critical artifact in the software development. All phases in software development are influenced by this artifact. Defects in software requirements may higher the risk of project overschedule that contributes to cost overrun of the project.Researchers have shown that finding defects in the initial software development phase is important becausethe cost of the bug is cheaper if it is fixed early. Hence, our main goal is to provide a platform for requirement engineers to produce better requirement specifications. We propose AmbiDetect, a (prototype) tool toautomatically classify ambiguous software requirements. AmbiDetect combines text mining and machine learning for ambiguous requirement specification detection. The text mining technique is used to extract classification features as well as generating the training set.AmbiDetect usesa machine learning technique to perform the ambiguous requirement specification detection. From an initial user study to validate the tool, the result indicates that the accuracy of detection is reasonably acceptable.Although AmbiDetect is an early experimental tool, we optimist that this tool can be a platform to improve SRS quality.
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POO, DANNY C. C. "AN OBJECT-ORIENTED SOFTWARE REQUIREMENTS ANALYSIS METHOD." International Journal of Software Engineering and Knowledge Engineering 02, no. 01 (1992): 145–68. http://dx.doi.org/10.1142/s0218194092000087.
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This paper discusses an object-oriented software requirements analysis method. The approach adopted here draws clear distinction between a system's basic structure (i.e. the object model) and its functionalities. The analysis model generated is a description of a problem domain; it consists of a set of primary and secondary objects that characterize the problem domain, and a set of pseudo objects that define the functional requirements of a system. There are two stages of analysis in the proposed method: Object Modelling and Functional Requirements Modelling. These two stages are built upon one another. The aim of the object modelling stage is to derive a model of the problem domain in terms of objects, their classification and inter-relationships with one another. The functional requirements modelling stage builds upon this initial object model to complete the requirement analysis specification. This paper uses a real-life library environment to illustrate how the method can be applied in the specification of an object-oriented software system.
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Zieliński, Zbigniew, Jan Chudzikiewicz, Janusz Furtak, Andrzej Stasiak, and Marek Brudka. "Secured Workstation to Process the Data of Different Classification Levels." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2012): 5–12. http://dx.doi.org/10.26636/jtit.2012.3.1273.
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The paper presents some of the results obtained within the ongoing project related with functional requirements and design models of secure workstation for special applications (SWSA). SWSA project is directed toward the combination of the existing hardware and software virtualization with cryptography and identification technologies to ensure the security of multilevel classified data by means of some formal methods. In the paper the requirements for SWSA, its hardware and software architecture, selected security solution for data processing and utilized approach to designing secure software are presented. The novel method for secure software design employs dedicated tools to verify the confidentiality and the integrity of data using Unified Modeling Language (UML) models. In general, the UML security models are embedded in and simulated with the system architecture models, thus the security problems in SWSA can be detected early during the software design. The application of UML topology models enables also to verify the fundamental requirement for MLS systems, namely the hardware isolation of subjects from different security domains.
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Jogannagari, Malla Reddy* S.V.A.V. Prasad Sambasiva Rao Baragada. "REQUIREMENT ENGINEERING : AN ARCHETYPAL APPROACH FOR THE DEVELOPMENT OF QUALITY SOFTWARE." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 12 (2016): 177–84. https://doi.org/10.5281/zenodo.225413.
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Requirement Engineering is the crucial phase of software development life cycle. The activity of requirement engineering is contract between the client and developer. The gathering of complete and consistent requirements can motive the quality of software product and can satisfy the user’s needs. The requirement engineering is a complex exercise that consider the product requirement demands from the number of viewpoints , roles and responsibilities. The successful systematic implementation of requirement engineering process can have good impact on the quality of software product.. In this research paper, we highlights the role of requirement engineering and its activities in the development of quality software product.
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Subahi, Ahmad F. "Enhancing Software Sustainability: Leveraging Large Language Models to Evaluate Security Requirements Fulfillment in Requirements Engineering." Systems 13, no. 2 (2025): 114. https://doi.org/10.3390/systems13020114.
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In the digital era, cybersecurity is integral for preserving national security, digital privacy, and social sustainability. This research emphasizes the role of non-functional equirements (NFRs) in developing secure software systems that enhance societal wellbeing by ensuring data protection, user privacy, and system robustness. Specifically, this study introduces a proof-of-concept approach by leveraging machine learning (ML) models to classify NFRs and identify security-related issues early in the software development lifecycle. Two experiments were conducted to assess the effectiveness of different models for binary and multi-class classification tasks. In Experiment 1, BERT-based models and artificial neural networks (ANNs) were fine-tuned to classify NFRs into security and non-security categories using a dataset of 803 statements. BERT-based models outperformed ANNs, achieving higher accuracy, precision, recall, and ROC-AUC scores, with hyperparameter tuning further enhancing the results. Experiment 2 assessed logistic regression (LR), a support vector machine (SVM), and XGBoost for the multi-class classification of security-related NFRs into seven categories. The SVM and XGBoost showed strong performance, achieving high precision and recall in specific categories. The findings demonstrate the effectiveness of advanced ML models in automating NFR classification, improving software security, and supporting social sustainability. Future work will explore hybrid approaches to enhance scalability and accuracy.
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Liao, Zhengbowen. "A study of the classification and main functions of BIM software." Highlights in Science, Engineering and Technology 18 (November 13, 2022): 230–38. http://dx.doi.org/10.54097/hset.v18i.2679.
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This paper starts by studying the seven characteristics of BIM and the essential functions of BIM software and divides BIM software into three categories, modelling software, functional software, and platform software. The definitions and basic applicable requirements of the three classifications of software are given, the detailed functions of the commonly used software in the three classifications are introduced, and the classification of commonly used BIM software in China is summarized to solve the problem of correct selection of BIM software in practical applications by engineers and technicians, and to facilitate the understanding and learning of relevant BIM software by engineers.
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Daniel, Mworia, Nderu Lawrence, and Kimwele Michael. "Embedding Quality into Software Product Line Variability Artifacts." International Journal of Software Engineering & Applications 12, no. 3 (2021): 11–25. http://dx.doi.org/10.5121/ijsea.2021.12302.
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The success of any software product line development project is closely tied to its domain variability management. Whereas a lot of effort has been put into functional variability management by the SPL community, non-functional variability is considered implicit. The result has been dissatisfaction among clients due to resultant poor quality systems. This work presents an integrated requirement specification template for quality and functional requirements at software product line variation points. The implementation of this approach at the analytical description phase increases the visibility of quality requirements obliging developers to implement them. The approach proposes the use of decision tree classification techniques to support the weaving of functional quality attributes at respective variation points. This work, therefore, promotes software product line variability management objectives by proposing new functional quality artifacts during requirements specification phase. The approach is illustrated with an exemplar mobile phone family data storage requirements case study.
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Mworia, Daniel, Lawrence Nderu, and Michael Kimwele. "Embedding Quality into Software Product Line Variability Artifacts." International Journal of Software Engineering & Applications (IJSEA) 12, no. 2/3 (2021): 11–25. https://doi.org/10.5281/zenodo.4983204.
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The success of any software product line development project is closely tied to its domain variability management. Whereas a lot of effort has been put into functional variability management by the SPL community, non-functional variability is considered implicit. The result has been dissatisfaction among clients due to resultant poor quality systems. This work presents an integrated requirement specification template for quality and functional requirements at software product line variation points. The implementation of this approach at the analytical description phase increases the visibility of quality requirements obliging developers to implement them. The approach proposes the use of decision tree classification techniques to support the weaving of functional quality attributes at respective variation points. This work, therefore, promotes software product line variability management objectives by proposing new functional quality artifacts during requirements specification phase. The approach is illustrated with an exemplar mobile phone family data storage requirements case study.
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Alemneh, Esubalew, and Fekerte Berhanu. "Software Requirement Smells and Detection Techniques: A Systematic Literature Review." Cybernetics and Information Technologies 24, no. 4 (2024): 78–107. https://doi.org/10.2478/cait-2024-0037.
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Abstract One of the major reasons for software project failure is poor requirements, so numerous requirement smells detection solutions are proposed. Critical appraisal of the proposed requirement fault detection methods is crucial for refining knowledge of requirement smells and developing new research ideas. The objective of this paper was to systematically review studies that focused on detecting requirement discrepancies in textual requirements. After applying inclusion and exclusion criteria and forward and backward snowball sampling techniques using database-specific search queries, 19 primary studies were selected. A deep analysis of the studies shows that classical NLP-based requirement smells detection techniques are the most commonly used ones and ambiguity is the requirement smell that has the utmost attention. Further investigation depicts the scarcity of open-access datasets, and tools employed to detect requirement faults. The review has also revealed there is no comprehensive definition and classification of requirement smells.
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García, S. E. Martínez, C. Alberto Fernández-y-Fernández, and E. G. Ramos Pérez. "Classification of Non-functional Requirements Using Convolutional Neural Networks." Programming and Computer Software 49, no. 8 (2023): 705–11. http://dx.doi.org/10.1134/s0361768823080133.
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Xu, Xiao, LiJuan Wang, RuFan Liu, and TianYu Xu. "Deep learning based news text classification software design." Journal of Physics: Conference Series 2031, no. 1 (2021): 012067. http://dx.doi.org/10.1088/1742-6596/2031/1/012067.
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Abstract New technologies such as artificial intelligence have developed at a rapid pace in recent years and are increasingly being used in the process of managing news in bulk. The development of deep learning has facilitated unprecedented progress in the field of computing and has opened our eyes to the possibility of using AI for news text classification. In this paper, based on the system requirements analysis, we describe the process of functional modules arising from the requirements analysis, design the internal details of functional modules, including algorithms and detailed principles, and finally obtain a prototype of news text classification software, which results in the pre-design expectations. The research in this paper makes the system development work more concrete, while providing software users, software developers, and analysts and testers with a unified and comprehensive understanding of the system’s functional implementation.
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Martinez Garcia, Sandra Estefanía, Carlos Alberto Fernández-Y-Fernández, and Erik G. Ramos Pérez. "Deep Learning for Non-functional Requirements: A Convolutional Neural Network Approach." Proceedings of the Institute for System Programming of the RAS 36, no. 1 (2024): 131–42. http://dx.doi.org/10.15514/ispras-2024-36(1)-8.
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The Requirements Engineering (ER) phase plays a critical role in software development, as any shortcomings during this stage can lead to project failure. Analysts rely on Requirements Specification (RS) to define a comprehensive list of quality requirements. The process of requirements classification, within RS, involves assigning each requirement to its respective class, presenting analysts with the challenge of accurate categorization. This research focuses on enhancing the classification of non-functional requirements (NFR) using a Convolutional Neural Network (CNN). The study also emphasizes the significance of preprocessing techniques, the implementation of sampling strategies, and the incorporation of pre-trained word embeddings such as Fasttext, Glove, and Word2vec. Evaluation of the proposed approach is performed using metrics like Recall, Precision, and F1, resulting in an average performance improvement of up to 30% compared to related work. Additionally, the model is assessed concerning its utilization of pre-trained word embeddings through ANOVA analysis, providing valuable insights into its effectiveness. This study aims to demonstrate the utility of CNNs and pre-trained word embeddings in the classification of NFRs, offering valuable contributions to the field of Requirements Engineering and enhancing the overall software development process.
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Muhamad, Fachrul Pralienka Bani, Esti Mulyani, Munengsih Sari Bunga, and Achmad Farhan Mushafa. "Class Balancing Methods Comparison for Software Requirements Classification on Support Vector Machines." SinkrOn 8, no. 2 (2023): 1196–208. http://dx.doi.org/10.33395/sinkron.v8i2.12415.
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Cost, time, and development effort can increase due to errors in analyzing functional and non-functional software requirements. To minimize these errors, previous research has tried to classify software requirements, especially non-functional requirements, on the PROMISE dataset using the Bag of Words (BoW) feature extraction and the Support Vector Machine (SVM) classification algorithm. On the other hand, the unbalanced distribution of class labels tends to decrease the evaluation result. Moreover, most software requirements are usually functional requirements. Therefore, there is a tendency for classifier models to classify test data as functional requirements. Previous research has performed class balancing on a dataset to handle unbalanced data. The study can achieve better classification evaluation results. Based on the previous research, this study proposes to combine the class balancing method and the SVM algorithm. K-fold cross-validation is used to optimize the training and test data to be more consistent in developing the SVM model. Tests were carried out on the value of K in k-fold, i.e., 5, 10, and 15. Results are measured by accuracy, f1-score, precision, and recall. The Public Requirements (PURE) dataset has been used in this research. Results show that SVM with class balancing can classify software requirements more accurately than SVM without class balancing. Random Over Sampling is the class balancing method with the highest evaluation score for classifying software requirements on SVM. The results showed an improvement in the average value of accuracy, f1 score, precision, and recall in SVM by 22.07%, 19.67%, 17.73%, and 19.67%, respectively.
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Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning." Entropy 23, no. 10 (2021): 1264. http://dx.doi.org/10.3390/e23101264.
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Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)—in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification.
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Devendra Kumar. "Classification of Non-Functional Requirements Using Semi-Supervised Learning Approach." Journal of Information Systems Engineering and Management 10, no. 16s (2025): 451–67. https://doi.org/10.52783/jisem.v10i16s.2633.
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The primary emphasis is on specialized work involving software engineers. Nevertheless, it is equally imperative to document the deficient features of software design, including aspects such as maintainability, reusability, and reliability. In rapid software development methodologies, such as agile, non-functional requirements (NFRs) are frequently neglected. This neglect results in an increased significance of eliminating non-functional requirements in agile-based software and a heightened emphasis on critical tasks during software migration. Misinterpretation of NFRs can be a significant factor contributing to project failure. A computer network simplifies the implementation of 12 key concepts such as NFR heuristics in the context of failing rules. We propose a semi-written classification system to identify useless needs. In this manner, the initial distribution for the NFR is learned using the small set determined during the heuristic phase. This iterative process helps identify additional needs. The aim was to incorporate this approach into individual recommendations to assist analysts and software designers in the architectural process. Using a semi-supervised learning method, NFRs can be effectively identified and classified. In addition, using other information provided by well-written and informal rules allows classification using fewer preexisting methods. The learning process improves the classification performance by leveraging user feedback during training. Our partial maintenance strategy provides over 70% accuracy compared with traditional maintenance using pre-recorded data. This demonstrates the superiority of the semi supervised approach. The researchers also noted that partial care systems require less human effort to enroll than full care and could be further developed according to the guidelines. Currently, our method is better than other distribution tracking methods, and we believe it will improve the performance of collaboration with input from analyst participants.
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Qureshi, Muhammad Shahroz Gul, Bilal Khan, and Muhammad Arshad. "ML-Based Model for Risk Prediction in Software Requirements." International Journal of Technology Diffusion 13, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijtd.314235.
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Software risk prediction is the most sensitive and crucial activity of the SDLC. It may lead to the success or failure of the project. The requirement gathering stage is the most important and challenging stage of the SDLC. The risks should be tackled at this stage and saved to be used in future projects. However, a model is proposed for the prediction of software requirement risks using the requirement risk dataset and ML classification. This research study proposed a model for risk prediction in software requirements that will be evaluated using several evaluation measures (e.g., precision, F-measure, MCC, recall, and accuracy). For the completion of this study, the dataset is taken from Zenodo repository. The model is evaluated using ML techniques. After the finding and analysis of results, DT shows best performance with accuracy of 99%.
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Talele, Pratvina, Siddharth Apte, Rashmi Phalnikar, and Harsha Talele. "Semi-automated Software Requirements Categorisation using Machine Learning Algorithms." International journal of electrical and computer engineering systems 14, no. 10 (2023): 1107–14. http://dx.doi.org/10.32985/ijeces.14.10.3.
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Requirement engineering is a mandatory phase of the Software development life cycle (SDLC) that includes defining and documenting system requirements in the Software Requirements Specification (SRS). As the complexity increases, it becomes difficult to categorise the requirements into functional and non-functional requirements. Presently, the dearth of automated techniques necessitates reliance on labour-intensive and time-consuming manual methods for this purpose. This research endeavours to address this gap by investigating and contrasting two prominent feature extraction techniques and their efficacy in automating the classification of requirements. Natural language processing methods are used in the text pre-processing phase, followed by the Term Frequency – Inverse Document Frequency (TF-IDF) and Word2Vec for feature extraction for further understanding. These features are used as input to the Machine Learning algorithms. This study compares existing machine learning algorithms and discusses their correctness in categorising the software requirements. In our study, we have assessed the algorithms Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Neural Network (NN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) on the precision and accuracy parameters. The results obtained in this study showed that the TF-IDF feature selection algorithm performed better in categorising requirements than the Word2Vec algorithm, with an accuracy of 91.20% for the Support Vector Machine (SVM) and Random Forest algorithm as compared to 87.36% for the SVM algorithm. A 3.84% difference is seen between the two when applied to the publicly available PURE dataset. We believe these results will aid developers in building products that aid in requirement engineering.
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Ali, Tariq, Saif Ur Rehman, Asif Nawaz, and Munir Ahmed. "Enhancing Reusability: An Integrated Framework for Software Requirements Classification and Prioritization." International Journal of Software Engineering and Knowledge Engineering 32, no. 09 (2022): 1453. http://dx.doi.org/10.1142/s0218194022930019.
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Talele, Pratvina, and Rashmi Phalnikar. "Multiple correlation based decision tree model for classification of software requirements." International Journal of Computational Science and Engineering 26, no. 3 (2023): 305–15. http://dx.doi.org/10.1504/ijcse.2023.10056915.
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Talele, Pratvina, and Rashmi Phalnikar. "Multiple correlation based decision tree model for classification of software requirements." International Journal of Computational Science and Engineering 26, no. 3 (2023): 305–15. http://dx.doi.org/10.1504/ijcse.2023.131502.
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Verma, Vikas, and Manish Jain. "OPTIMIZATION OF ROUTING USING TRAFFIC CLASSIFICATION IN SOFTWARE DEFINED NETWORKING." Suranaree Journal of Science and Technology 30, no. 1 (2023): 010198(1–8). http://dx.doi.org/10.55766/sujst-2023-01-e01890.
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Efficient routing is an essential task for any network as it directly impacts the network’s performance. In this paper, we have used traffic classification techniques to optimize routing in Software Defined Network (SDN) and provided a cost aware routing framework. Instead of classifying traffic based on a particular parameter, we perform the traffic classification using a hybrid approach that is based on machine learning techniques. Our framework uses supervised machine learning techniques to classify network flow and detect elephant flows which are too heavy in size and require significant bandwidth. As the requirements of each elephant flow vary with the application, we further perform Quality of Service (QoS) based traffic classification, which classifies these elephant flow into QoS classes as per their QoS requirements. For this purpose, we have used semi-supervised machine learning algorithms. Further, we have proposed a routing algorithm that makes use of the Dijkstra algorithm to compute the best possible shortest path based on the QoS requirements of the elephant flow. The proposed method was implemented and tested on Mininet using a POX controller. The simulation results show that our framework successfully classifies elephant flows with an accuracy of 80% and also computes a low-cost path for each elephant flow based on the actual internet data set.
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Ko, Youngjoong, Sooyong Park, Jungyun Seo, and Soonhwang Choi. "Using classification techniques for informal requirements in the requirements analysis-supporting system." Information and Software Technology 49, no. 11-12 (2007): 1128–40. http://dx.doi.org/10.1016/j.infsof.2006.11.007.
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Skrypnikov, A. V., V. A. Khvostov, E. V. Chernyshova, V. V. Samtsov, and M. A. Abasov. "Rationing requirements to the characteristics of software tools to protect information." Proceedings of the Voronezh State University of Engineering Technologies 80, no. 4 (2019): 96–110. http://dx.doi.org/10.20914/2310-1202-2018-4-96-110.
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The article is devoted to the solution of the scientific problem of the development of theoretical foundations and technology of substantiation of quantitative requirements (rules) for software information security (PSI). The basis of the modern theory of information security is a classification approach. When using the classification approach, the requirements for PSSS are defined as a set of functional requirements necessary for implementation for a certain class of security. At the same time, the concept of "effectiveness of information protection" is not considered. The contradiction between the qualitative classification approach in the formation of requirements for PSI and the need to use their quantitative characteristics in the development of automated systems (as) in protected execution required the development of a new normative approach to substantiate the requirements for information protection. Normative approach based on the systematic consideration of problems in which the analysis of interaction of elements as each other and the influence of PSSI on the AU in General and the analysis of the goals of security of information (BI). The information structure of the system is constructed on the basis of the analysis of the AU topology, internal and external relations and information flows. At the same time, the normative method considers the full set of BI threats. BI threats are stochastic, multi-stage and multi-variant. In turn, the NSCI in implementing protection functions neutralizes BI threats with some probability (there are residual risks) and length in time. The presence of a variety of BI threats, characterized by different time of implementation, probabilistic characteristics of overcoming PSI and destructive capabilities, require the finding of BI norms by optimization methods, based on the requirements of minimizing the impact on the efficiency of the automated system.
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Castillo-Valdivieso, Pedro A., Esraa Alhenawi, Shatha Awawdeh, Ruba Abu Khurma, Maribel García-Arenas, and Amjad Hudaib. "Choosing a Suitable Requirement Prioritization Method: A Survey." Journal of Computer Science and Technology 24, no. 1 (2024): e04. http://dx.doi.org/10.24215/16666038.24.e04.
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Software requirements prioritization plays a crucial role in software development. It can be viewed as the process of ordering requirements by determining which requirements must be done first and which can be done later. Powerful requirements prioritization techniques are of paramount importance to finish the implementation on time and budget. Many factors affect requirement prioritization such as stakeholder expectations, complexity, dependency, scalability, risk and cost. Therefore, finding the proper order of requirements is a challenging process. Hence, different types of requirements prioritization techniques have been developed to support this task. In this survey we propose a novel classification that can classify the prioritization techniques under two major classes: relative and exact prioritization techniques class where each class is divided into two subclasses. We also provide an overview about fifteen different requirements prioritization techniques that are classified under our proposed classification. Moreover, we make a comparison between methods that are related to the same subclass to analyze their strengths and weakness. Based on the comparison results, the properties for each proposed subclass of techniques are identified. Depending on these properties, we present some recommendations to help project managers in the process of selection the most suitable technique to prioritize requirements based on their project characteristics.
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Nor, Hidayah Zainal Abidin, and Abdul Samat Pathiah. "Requirements identification for distributed agile team communication using high level carotene." Bulletin of Electrical Engineering and Informatics 10, no. 1 (2021): 249–57. https://doi.org/10.11591/eei.v10i1.2031.
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Communication plays an important role to deliver the correct information. However, the communication became challenging especially for agile software teams, which are in geographical distributed. The problem arise when there are exchanging information using unstructured communication platform, misunderstanding on the information communicated and lack of documentation. The aim of this study is to propose a text classification technique for requirements identification in text messages. In this study, we adopted the cascade and cluster classification concept of Carotene that relies on the hash tag function. It classifies the text messages into requirements types instead of job title. This technique called as high-level carotene (HLC) technique that embedded into the tool to identify the functional requirement and non-functional requirements. The result shows that most of criterias evaluated have achieved more than 85% of effectiveness in identifying both of requirement in text messaging by using this technique.
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Gangadharan, G. R., Lorna Uden, and Paul Oude Luttighuis. "Sourcing Requirements and Designs for Software as a Service." International Journal of Systems and Service-Oriented Engineering 6, no. 1 (2016): 1–16. http://dx.doi.org/10.4018/ijssoe.2016010101.
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Software as a Service (SaaS) has become an important pragmatic in the world of enterprise software and business services markets. SaaS supports the concept of outsourcing where business processes are offered under a service level agreement for a given price. However, sourcing SaaS may not always involve outsourcing with respect to the transfer of internal activities and resources to external service providers. Users of SaaS need to know what strategies to use when determining sourcing requirements. In this paper, the authors develop a classification for sourcing SaaS based on Kraljic's matrix and a mapping of SaaS services to the sourcing structures. Further, they evaluate the proposed sourcing models against two real world case studies.