Journal articles: 'Computer software. Software engineering. Machine learning'

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Hussain*, Mandi Akif, Revoori Veeharika Reddy, Kedharnath Nagella, and Vidya S. "Software Defect Estimation using Machine Learning Algorithms." International Journal of Recent Technology and Engineering 10, no. 1 (May 30, 2021): 204–8. http://dx.doi.org/10.35940/ijrte.a5898.0510121.

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Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.

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Bera, Debjyoti, Mathijs Schuts, Jozef Hooman, and Ivan Kurtev. "Reverse engineering models of software interfaces." Computer Science and Information Systems 18, no. 3 (2021): 657–86. http://dx.doi.org/10.2298/csis200131013b.

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Cyber-physical systems consist of many hardware and software components. Over the lifetime of these systems their components are often replaced or updated. To avoid integration problems, formal specifications of component interface behavior are crucial. Such a formal specification captures not only the set of provided operations but also the order of using them and the constraints on their timing behavior. Usually the order of operations are expressed in terms of a state machine. For new components such a formal specification can be derived from requirements. However, for legacy components such interface descriptions are usually not available. So they have to be reverse engineered from existing event logs and source code. This costs a lot of time and does not scale very well. To improve the efficiency of this process, we present a passive learning technique for interface models inspired by process mining techniques. The approach is based on representing causal relations between events present in an event log and their timing information as a timed-causal graph. The graph is further processed and eventually transformed into a state machine and a set of timing constraints. Compared to other approaches in literature which focus on the general problem of inferring state-based behavior, we exploit patterns of client-server interactions in event logs.

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Chung, Chih-Ko, and Pi-Chung Wang. "Version-Wide Software Birthmark via Machine Learning." IEEE Access 9 (2021): 110811–25. http://dx.doi.org/10.1109/access.2021.3103186.

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Al Sghaier, Hiba. "RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW." International Journal of Engineering Technologies and Management Research 7, no. 6 (June 15, 2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v2020.i7.6.694.

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Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries. Software engineers are aware of programming languages that are employed to develop software systems, by applying engineering principles to development process; they can design customized software systems for individual or organizational customers. The new trends in software engineering are numerous, Cloud Computing, machine learning, deep learning, big Data, mobile Computing. Nevertheless, there are many more other research trends in software engineering's field that have been intensively explored and implemented in many different industries. In this paper, authors try to summarize the most fields that are integrated with software engineering recently.

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Al Sghaier, Hiba. "RESEARCH TRENDS IN SOFTWARE ENGINEERING FIELD: A LITERATURE REVIEW." International Journal of Engineering Technologies and Management Research 7, no. 6 (June 15, 2020): 58–65. http://dx.doi.org/10.29121/ijetmr.v7.i6.2020.694.

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Software engineering is one of computer science branches, it comprises of building and developing software systems and applications. Software engineering is a discipline that has a constant growth in research in aim to identify new technologies and adopt it in different areas; there is a considerable investment on software engineering trends at the current time due to the availability of mobile technologies. With millions of billions of smart devices that are connected to the internet, all industries around the world are rapidly becoming a technology driven industries. Software engineers are aware of programming languages that are employed to develop software systems, by applying engineering principles to development process; they can design customized software systems for individual or organizational customers. The new trends in software engineering are numerous, Cloud Computing, machine learning, deep learning, big Data, mobile Computing. Nevertheless, there are many more other research trends in software engineering's field that have been intensively explored and implemented in many different industries. In this paper, authors try to summarize the most fields that are integrated with software engineering recently.

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Saputri, Theresia Ratih Dewi, and Seok-Won Lee. "Software Analysis Method for Assessing Software Sustainability." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (January 2020): 67–95. http://dx.doi.org/10.1142/s0218194020500047.

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Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability achievement. Moreover, there exist limited quantifiable methods for supporting software sustainability evaluation. Our primary objective is to present a methodology that can assist software engineers in evaluating a software system based on well-defined sustainability metrics and measurements. We propose a novel approach that combines machine learning (ML) and software analysis methods. To simplify the application of the proposed approach, we present a semi-automated tool that supports engineers in assessing the sustainability achievement of a software system. The results of our study demonstrate that the proposed approach determines sustainability criteria and defines sustainability achievement in terms of a traceable matrix. Our theoretical evaluation and empirical study demonstrate that the proposed support tool can help engineers identify sustainability limitations in a particular feature of a software system. Our semi-automated tool can identify features that must be revised to enhance sustainability achievement.

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BAILIN, SIDNEY C., ROBERT H. GATTIS, and WALT TRUSZKOWSKI. "A LEARNING-BASED SOFTWARE ENGINEERING ENVIRONMENT FOR REUSING DESIGN KNOWLEDGE." International Journal of Software Engineering and Knowledge Engineering 01, no. 04 (December 1991): 351–71. http://dx.doi.org/10.1142/s0218194091000251.

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As part of the NASA/Goddard Code 522.3 research program in software engineering, a Knowledge-Based Software Engineering Environment (KBSEE) is being developed. The KBSEE will support a comprehensive artifact-reuse capability and will incorporate knowledge-based concepts such as machine learning and design knowledge capture. The distinguishing features of this work are that it is a systematic approach to the reuse of knowledge, not just of products, and it implements learning as an explicitly supported function in a software engineering environment. Each of these objectives is currently being pursued in a distinct prototype environment: design knowledge capture and knowledge reuse in KAPTUR (Knowledge Acquisition for Preservation of Tradeoffs and Underlying Rationales), and learning in LEARN (Learning Enhanced Automation of Reuse Engineering). Despite their prototype realization in different environments, the integration of these approaches into an overall KBSEE is a key goal of our work.

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Siewruk, Grzegorz, and Wojciech Mazurczyk. "Context-Aware Software Vulnerability Classification Using Machine Learning." IEEE Access 9 (2021): 88852–67. http://dx.doi.org/10.1109/access.2021.3075385.

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Firdaus Zainal Abidin, Ahmad, Mohd Faaizie Darmawan, Mohd Zamri Osman, Shahid Anwar, Shahreen Kasim, Arda Yunianta, and Tole Sutikno. "Adaboost-multilayer perceptron to predict the student’s performance in software engineering." Bulletin of Electrical Engineering and Informatics 8, no. 4 (December 1, 2019): 1556–62. http://dx.doi.org/10.11591/eei.v8i4.1432.

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Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students.

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AZAR, DANIELLE. "A GENETIC ALGORITHM FOR IMPROVING ACCURACY OF SOFTWARE QUALITY PREDICTIVE MODELS: A SEARCH-BASED SOFTWARE ENGINEERING APPROACH." International Journal of Computational Intelligence and Applications 09, no. 02 (June 2010): 125–36. http://dx.doi.org/10.1142/s1469026810002811.

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In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.

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Medeiros, Nadia, Naghmeh Ivaki, Pedro Costa, and Marco Vieira. "Vulnerable Code Detection Using Software Metrics and Machine Learning." IEEE Access 8 (2020): 219174–98. http://dx.doi.org/10.1109/access.2020.3041181.

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De Carvalho, Halcyon Davys Pereira, Roberta Fagundes, and Wylliams Santos. "Extreme Learning Machine Applied to Software Development Effort Estimation." IEEE Access 9 (2021): 92676–87. http://dx.doi.org/10.1109/access.2021.3091313.

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Ganapathy, Apoorva, and Taposh Kumar Neogy. "Artificial Intelligence Price Emulator: A Study on Cryptocurrency." Global Disclosure of Economics and Business 6, no. 2 (December 31, 2017): 115–22. http://dx.doi.org/10.18034/gdeb.v6i2.558.

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The cryptocurrency Artificial intelligence price emulator is a software programmed to collect cryptocurrency market data, analyze the data and predict the market price using the collected data. Computer emulators are programmed to mimic and copy behaviors or other software/hardware. The reason for emulation is to get to a particular result as quickly as possible. Machine learning is the ability of computers to read and process data while learning from the data with human interference or influence. This work focused majorly on how cryptocurrency market prices can be emulated using Artificial Intelligence with machine learning abilities. It also looked into the advantages of using the software for crypto investors. Some of which is the reduced time of research, reduction of risk, among others.

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Pandey, Sushant Kumar, Ravi Bhushan Mishra, and Anil Kumar Tripathi. "Machine learning based methods for software fault prediction: A survey." Expert Systems with Applications 172 (June 2021): 114595. http://dx.doi.org/10.1016/j.eswa.2021.114595.

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Rodríguez-Gracia, Diego, José A. Piedra-Fernández, Luis Iribarne, Javier Criado, Rosa Ayala, Joaquín Alonso-Montesinos, and Capobianco-Uriarte Maria de las Mercedes. "Microservices and Machine Learning Algorithms for Adaptive Green Buildings." Sustainability 11, no. 16 (August 9, 2019): 4320. http://dx.doi.org/10.3390/su11164320.

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In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings.

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Zheng, Wei, Yutong Bai, and Haoxuan Che. "A computer-assisted instructional method based on machine learning in software testing class." Computer Applications in Engineering Education 26, no. 5 (June 28, 2018): 1150–58. http://dx.doi.org/10.1002/cae.21962.

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Mahdi, Mohammed Najah, Mohd Hazli Mohamed Zabil, Abdul Rahim Ahmad, Roslan Ismail, Yunus Yusoff, Lim Kok Cheng, Muhammad Sufyian Bin Mohd Azmi, Hayder Natiq, and Hushalini Happala Naidu. "Software Project Management Using Machine Learning Technique—A Review." Applied Sciences 11, no. 11 (June 2, 2021): 5183. http://dx.doi.org/10.3390/app11115183.

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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Perlovsky, Leonid, and Gary Kuvich. "Machine Learning and Cognitive Algorithms for Engineering Applications." International Journal of Cognitive Informatics and Natural Intelligence 7, no. 4 (October 2013): 64–82. http://dx.doi.org/10.4018/ijcini.2013100104.

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Mind is based on intelligent cognitive processes, which are not limited by language and logic only. The thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures, and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized. Semiotic approach allows for the universal representation of such diagrams. In that approach, logic is a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. The most efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the “bottom” to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the “top” of mental hierarchy. Knowledge Instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. Future research directions are discussed.

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Girard, Simon R., Vincent Legault, Guy Bois, and Jean-François Boland. "Avionics Graphics Hardware Performance Prediction with Machine Learning." Scientific Programming 2019 (June 3, 2019): 1–15. http://dx.doi.org/10.1155/2019/9195845.

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Within the strongly regulated avionic engineering field, conventional graphical desktop hardware and software application programming interface (API) cannot be used because they do not conform to the avionic certification standards. We observe the need for better avionic graphical hardware, but system engineers lack system design tools related to graphical hardware. The endorsement of an optimal hardware architecture by estimating the performance of a graphical software, when a stable rendering engine does not yet exist, represents a major challenge. As proven by previous hardware emulation tools, there is also a potential for development cost reduction, by enabling developers to have a first estimation of the performance of its graphical engine early in the development cycle. In this paper, we propose to replace expensive development platforms by predictive software running on a desktop computer. More precisely, we present a system design tool that helps predict the rendering performance of graphical hardware based on the OpenGL Safety Critical API. First, we create nonparametric models of the underlying hardware, with machine learning, by analyzing the instantaneous frames per second (FPS) of the rendering of a synthetic 3D scene and by drawing multiple times with various characteristics that are typically found in synthetic vision applications. The number of characteristic combinations used during this supervised training phase is a subset of all possible combinations, but performance predictions can be arbitrarily extrapolated. To validate our models, we render an industrial scene with characteristic combinations not used during the training phase and we compare the predictions to those real values. We find a median prediction error of less than 4 FPS.

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Radliński, Łukasz. "Predicting Aggregated User Satisfaction in Software Projects." Foundations of Computing and Decision Sciences 43, no. 4 (December 1, 2018): 335–57. http://dx.doi.org/10.1515/fcds-2018-0017.

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Abstract User satisfaction is an important feature of software quality. However, it was rarely studied in software engineering literature. By enhancing earlier research this paper focuses on predicting user satisfaction with machine learning techniques using software development data from an extended ISBSG dataset. This study involved building, evaluating and comparing a total of 15,600 prediction schemes. Each scheme consists of a different combination of its components: manual feature preselection, handling missing values, outlier elimination, value normalization, automated feature selection, and a classifier. The research procedure involved a 10-fold cross-validation and separate testing, both repeated 10 times, to train and to evaluate each prediction scheme. Achieved level of accuracy for best performing schemes expressed by Matthews correlation coefficient was about 0.5 in the cross-validation and about 0.5–0.6 in the testing stage. The study identified the most accurate settings for components of prediction schemes.

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Tiwari, Tanya, Tanuj Tiwari, and Sanjay Tiwari. "How Artificial Intelligence, Machine Learning and Deep Learning are Radically Different?" International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 2 (March 6, 2018): 1. http://dx.doi.org/10.23956/ijarcsse.v8i2.569.

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There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL). A computer system able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. Artificial Intelligence has made it possible. Deep learning is a subset of machine learning, and machine learning is a subset of AI, which is an umbrella term for any computer program that does something smart. In other words, all machine learning is AI, but not all AI is machine learning, and so forth. Machine Learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. Machine learning (ML)is a vibrant field of research, with a range of exciting areas for further development across different methods and applications. These areas include algorithmic interpretability, robustness, privacy, fairness, inference of causality, human-machine interaction, and security. The goal of ML is never to make “perfect” guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones. This paper gives an overview of artificial intelligence, machine learning & deep learning techniques and compare these techniques.

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NAKKRASAE, SATHIT, and PERAPHON SOPHATSATHIT. "AN RPCL-BASED INDEXING APPROACH FOR SOFTWARE COMPONENT CLASSIFICATION." International Journal of Software Engineering and Knowledge Engineering 14, no. 05 (October 2004): 497–518. http://dx.doi.org/10.1142/s0218194004001774.

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Software Engineering is not only a technical discipline of its own, but also a problem domain where technologies coming from other disciplines are relevant and can play important roles. One important example is knowledge engineering, a term that is used in a broad sense to encompass artificial intelligence, computational intelligence, knowledge bases, data mining, and machine learning [13]. Many typical software development issues can benefit from these disciplines. For this reason, we will employ computational intelligence approach to classify software component repository into similar component cluster groups with the help of Rival Penalized Competitive Learning algorithm. The center of each cluster will be used to construct the coarse grain classification indexing structure. Subsequent retrieval requirements of software components are compared with all the indexed cluster centers. Any software components belonging to the cluster partition whose center is closest to the required software component will be retrieved and participated in selecting the most suitable software component at the fine grain level. This approach not only is suitable for multi-dimensional data, but also automatically decides the correct model classification.

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Sheneamer, Abdullah M. "An Automatic Advisor for Refactoring Software Clones Based on Machine Learning." IEEE Access 8 (2020): 124978–88. http://dx.doi.org/10.1109/access.2020.3006178.

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Lo, Sin Kit, Qinghua Lu, Chen Wang, Hye-Young Paik, and Liming Zhu. "A Systematic Literature Review on Federated Machine Learning." ACM Computing Surveys 54, no. 5 (June 2021): 1–39. http://dx.doi.org/10.1145/3450288.

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Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.

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Cândido, Jeanderson, Maurício Aniche, and Arie van Deursen. "Log-based software monitoring: a systematic mapping study." PeerJ Computer Science 7 (May 6, 2021): e489. http://dx.doi.org/10.7717/peerj-cs.489.

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Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context.

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Akimova, Elena N., Alexander Yu Bersenev, Artem A. Deikov, Konstantin S. Kobylkin, Anton V. Konygin, Ilya P. Mezentsev, and Vladimir E. Misilov. "A Survey on Software Defect Prediction Using Deep Learning." Mathematics 9, no. 11 (May 24, 2021): 1180. http://dx.doi.org/10.3390/math9111180.

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Defect prediction is one of the key challenges in software development and programming language research for improving software quality and reliability. The problem in this area is to properly identify the defective source code with high accuracy. Developing a fault prediction model is a challenging problem, and many approaches have been proposed throughout history. The recent breakthrough in machine learning technologies, especially the development of deep learning techniques, has led to many problems being solved by these methods. Our survey focuses on the deep learning techniques for defect prediction. We analyse the recent works on the topic, study the methods for automatic learning of the semantic and structural features from the code, discuss the open problems and present the recent trends in the field.

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Twala, Bhekisipho. "Predicting Software Faults in Large Space Systems using Machine Learning Techniques." Defence Science Journal 61, no. 4 (July 28, 2011): 306–16. http://dx.doi.org/10.14429/dsj.61.1088.

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Bailin, Sidney, Scott Henderson, and Walt Truszkowski. "Application of machine learning to the organization of institutional software repositories." Telematics and Informatics 10, no. 3 (June 1993): 283–99. http://dx.doi.org/10.1016/0736-5853(93)90031-x.

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Sabir, Bushra, Faheem Ullah, M. Ali Babar, and Raj Gaire. "Machine Learning for Detecting Data Exfiltration." ACM Computing Surveys 54, no. 3 (June 2021): 1–47. http://dx.doi.org/10.1145/3442181.

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Context : Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. Objective : This article aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures. This review also aims at identifying gaps in research on ML-based data exfiltration countermeasures. Method : We used Systematic Literature Review (SLR) method to select and review 92 papers. Results : The review has enabled us to: (a) classify the ML approaches used in the countermeasures into data-driven, and behavior-driven approaches; (b) categorize features into six types: behavioral, content-based, statistical, syntactical, spatial, and temporal; (c) classify the evaluation datasets into simulated, synthesized, and real datasets; and (d) identify 11 performance measures used by these studies. Conclusion : We conclude that: (i) The integration of data-driven and behavior-driven approaches should be explored; (ii) There is a need of developing high quality and large size evaluation datasets; (iii) Incremental ML model training should be incorporated in countermeasures; (iv) Resilience to adversarial learning should be considered and explored during the development of countermeasures to avoid poisoning attacks; and (v) The use of automated feature engineering should be encouraged for efficiently detecting data exfiltration attacks.

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Bergadano, F., and D. Gunetti. "Learning relations and logic programs." Knowledge Engineering Review 9, no. 1 (March 1994): 73–77. http://dx.doi.org/10.1017/s0269888900006615.

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Inductive Logic Programming (ILP) is an emerging research area at the intersection of machine learning, logic programming and software engineering. The first workshop on this topic was held in 1991 in Portugal (Muggleton, 1991). Subsequently, there was a workshop tied to the Future Generation Computer System Conference in Japan in 1992, and a third one in Bled, Slovenia, in April 1993 (Muggleton, 1993). Ideas related to ILP are also appearing in major AI and machine learning conferences and journals. Although European-based and mainly sponsored by ESPRIT, ILP aims at becoming equally represented elsewhere; for example, among researchers in America who are investigating relational learning and first order theory revision (see, for example, the papers in Birnbaum and Collins, 1991) and within the computational learning theory community. This year's IJCAI workshop on ILP is a first step in this direction, and includes recent work with a broader range of perspectives and techniques.

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Naseem, Rashid, Zain Shaukat, Muhammad Irfan, Muhammad Arif Shah, Arshad Ahmad, Fazal Muhammad, Adam Glowacz, Larisa Dunai, Jose Antonino-Daviu, and Adel Sulaiman. "Empirical Assessment of Machine Learning Techniques for Software Requirements Risk Prediction." Electronics 10, no. 2 (January 14, 2021): 168. http://dx.doi.org/10.3390/electronics10020168.

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Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.

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P, Gouthaman, and Suresh Sankaranarayanan. "Prediction of Risk Percentage in Software Projects by Training Machine Learning Classifiers." Computers & Electrical Engineering 94 (September 2021): 107362. http://dx.doi.org/10.1016/j.compeleceng.2021.107362.

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Martin, Ignacio, Sebastian Troia, Jose Alberto Hernandez, Alberto Rodriguez, Francesco Musumeci, Guido Maier, Rodolfo Alvizu, and Oscar Gonzalez de Dios. "Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks." IEEE Transactions on Network and Service Management 16, no. 3 (September 2019): 871–83. http://dx.doi.org/10.1109/tnsm.2019.2927867.

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Vladlen, Devin, Tkachuk Vasil, and Skorobogatov Dmytro. "USAGE OF «GIM» SOFTWARE WHILE TEACHING "TECHNICAL MECHANICS" DISCIPLINE." OPEN EDUCATIONAL E-ENVIRONMENT OF MODERN UNIVERSITY, no. 7 (2019): 17–29. http://dx.doi.org/10.28925/2414-0325.2019.7.2.

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Technical Mechanics being a complex of fundamental general technical disciplines is the theoretical and scientific basis for the study and development of modern engineering. Using its laws and principles buildings, constructions, machines and equipment can be developed and researched. However, Technical Mechanics is the most difficult discipline to learn. The main difficulties of this course are based not only on the application of the principles of theoretical mechanics to complex mechanisms of different types but also the course stands out with its specific and particular features and also based on previously unknown terminology for students as well as is an introduction to the Mechanics of Machines in general. Difficulties in the acquisition of learning content are also caused by time constraints devoted to the study of this subject in higher technical educational institutions. Consequently, teaching Mechanical Engineering in today's context requires a widespread involvement of computer technologies into educational processes that meets the requirements of intensification of students’ individual study and diversifies forms and means of learning, motivates students and optimizes their learning process. The problem of the research is to identify the possibilities of intensifying the process of teaching Technical Mechanics which is a fundamental discipline for other special courses in Technical Institutes using computer training software GIM. The article justifies the feasibility of using GIM software during Technical Mechanics course by students of engineering and technical higher education institutions. GIM software is used during practical classes to support and supplement theoretical lectures; GIM functions as an educational supplementary tool to enhance students' knowledge. Training of first and second year students of general engineering disciplines based on GIM computer training program enhances the professional culture of the future specialist, stimulating his need for continuous self-improvement. New educational technologies are an effective method of teaching students and help better understanding of the subject, promotion and spreading of scientific knowledge

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Ashik, Mathew, A. Jyothish, S. Anandaram, P. Vinod, Francesco Mercaldo, Fabio Martinelli, and Antonella Santone. "Detection of Malicious Software by Analyzing Distinct Artifacts Using Machine Learning and Deep Learning Algorithms." Electronics 10, no. 14 (July 15, 2021): 1694. http://dx.doi.org/10.3390/electronics10141694.

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Malware is one of the most significant threats in today’s computing world since the number of websites distributing malware is increasing at a rapid rate. Malware analysis and prevention methods are increasingly becoming necessary for computer systems connected to the Internet. This software exploits the system’s vulnerabilities to steal valuable information without the user’s knowledge, and stealthily send it to remote servers controlled by attackers. Traditionally, anti-malware products use signatures for detecting known malware. However, the signature-based method does not scale in detecting obfuscated and packed malware. Considering that the cause of a problem is often best understood by studying the structural aspects of a program like the mnemonics, instruction opcode, API Call, etc. In this paper, we investigate the relevance of the features of unpacked malicious and benign executables like mnemonics, instruction opcodes, and API to identify a feature that classifies the executable. Prominent features are extracted using Minimum Redundancy and Maximum Relevance (mRMR) and Analysis of Variance (ANOVA). Experiments were conducted on four datasets using machine learning and deep learning approaches such as Support Vector Machine (SVM), Naïve Bayes, J48, Random Forest (RF), and XGBoost. In addition, we also evaluate the performance of the collection of deep neural networks like Deep Dense network, One-Dimensional Convolutional Neural Network (1D-CNN), and CNN-LSTM in classifying unknown samples, and we observed promising results using APIs and system calls. On combining APIs/system calls with static features, a marginal performance improvement was attained comparing models trained only on dynamic features. Moreover, to improve accuracy, we implemented our solution using distinct deep learning methods and demonstrated a fine-tuned deep neural network that resulted in an F1-score of 99.1% and 98.48% on Dataset-2 and Dataset-3, respectively.

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Moreb, Mohammed, Tareq Abed Mohammed, and Oguz Bayat. "A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems." IEEE Access 8 (2020): 23169–78. http://dx.doi.org/10.1109/access.2020.2970178.

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Waqar, Muhammad, Hassan Dawood, Hussain Dawood, Nadeem Majeed, Ameen Banjar, and Riad Alharbey. "An Efficient SMOTE-Based Deep Learning Model for Heart Attack Prediction." Scientific Programming 2021 (March 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/6621622.

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Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity of digital cardiac data. These data can be utilized for various beneficial purposes. These data’s utilization becomes more important when we are dealing with critical diseases like a heart attack where patient life is often at stake. Machine learning and deep learning are two famous techniques that are helping in making the raw data useful. Some of the biggest problems that arise from the usage of the aforementioned techniques are massive resource utilization, extensive data preprocessing, need for features engineering, and ensuring reliability in classification results. The proposed research work presents a cost-effective solution to predict heart attack with high accuracy and reliability. It uses a UCI dataset to predict the heart attack via various machine learning algorithms without the involvement of any feature engineering. Moreover, the given dataset has an unequal distribution of positive and negative classes which can reduce performance. The proposed work uses a synthetic minority oversampling technique (SMOTE) to handle given imbalance data. The proposed system discarded the need of feature engineering for the classification of the given dataset. This led to an efficient solution as feature engineering often proves to be a costly process. The results show that among all machine learning algorithms, SMOTE-based artificial neural network when tuned properly outperformed all other models and many existing systems. The high reliability of the proposed system ensures that it can be effectively used in the prediction of the heart attack.

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Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "An Ensemble Machine Learning Technique for Functional Requirement Classification." Symmetry 12, no. 10 (September 25, 2020): 1601. http://dx.doi.org/10.3390/sym12101601.

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In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.

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Morejón, Reinier, Marx Viana, and Carlos Lucena. "An Approach to Generate Software Agents for Health Data Mining." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1579–89. http://dx.doi.org/10.1142/s0218194017400125.

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Data mining is a hot topic that attracts researchers of different areas, such as database, machine learning, and agent-oriented software engineering. As a consequence of the growth of data volume, there is an increasing need to obtain knowledge from these large datasets that are very difficult to handle and process with traditional methods. Software agents can play a significant role performing data mining processes in ways that are more efficient. For instance, they can work to perform selection, extraction, preprocessing, and integration of data as well as parallel, distributed, or multisource mining. This paper proposes a framework based on multiagent systems to apply data mining techniques to health datasets. Last but not least, the usage scenarios that we use are datasets for hypothyroidism and diabetes and we run two different mining processes in parallel in each database.

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A G, Priya Varshini, Anitha Kumari K, and Vijayakumar Varadarajan. "Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach." Electronics 10, no. 10 (May 17, 2021): 1195. http://dx.doi.org/10.3390/electronics10101195.

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Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.

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Berselli, Giovanni, Pietro Bilancia, and Luca Luzi. "Project-based learning of advanced CAD/CAE tools in engineering education." International Journal on Interactive Design and Manufacturing (IJIDeM) 14, no. 3 (August 14, 2020): 1071–83. http://dx.doi.org/10.1007/s12008-020-00687-4.

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Abstract The use of integrated Computer Aided Design/Engineering (CAD/CAE) software capable of analyzing mechanical devices in a single parametric environment is becoming an industrial standard. Potential advantages over traditional enduring multi-software design routines can be outlined into time/cost reduction and easier modeling procedures. To meet industrial requirements, the engineering education is constantly revising the courses programs to include the training of modern advanced virtual prototyping technologies. Within this scenario, the present work describes the CAD/CAE project-based learning (PjBL) activity developed at the University of Genova as a part of course named Design of Automatic Machines, taught at the second level degree in mechanical engineering. The PjBL activity provides a detailed overview of an integrated design environment (i.e. PTC Creo). The students, divided into small work groups, interactively gain experience with the tool via the solution of an industrial design problem, provided by an engineer from industry. The considered case study consists of an automatic pushing device implemented in a commercial machine. Starting from a sub-optimal solution, the students, supervised by the lecturers, solve a series of sequential design steps involving both motion and structural analysis. The paper describes each design phase and summarizes the numerical outputs. At last, the results of the PjBL activity are presented and commented by considering the opinions of all the parties involved.

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Imran, Zeba Ghaffar, Abdullah Alshahrani, Muhammad Fayaz, Ahmed Mohammed Alghamdi, and Jeonghwan Gwak. "A Topical Review on Machine Learning, Software Defined Networking, Internet of Things Applications: Research Limitations and Challenges." Electronics 10, no. 8 (April 7, 2021): 880. http://dx.doi.org/10.3390/electronics10080880.

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In recent years, rapid development has been made to the Internet of Things communication technologies, infrastructure, and physical resources management. These developments and research trends address challenges such as heterogeneous communication, quality of service requirements, unpredictable network conditions, and a massive influx of data. One major contribution to the research world is in the form of software-defined networking applications, which aim to deploy rule-based management to control and add intelligence to the network using high-level policies to have integral control of the network without knowing issues related to low-level configurations. Machine learning techniques coupled with software-defined networking can make the networking decision more intelligent and robust. The Internet of Things application has recently adopted virtualization of resources and network control with software-defined networking policies to make the traffic more controlled and maintainable. However, the requirements of software-defined networking and the Internet of Things must be aligned to make the adaptations possible. This paper aims to discuss the possible ways to make software-defined networking enabled Internet of Things application and discusses the challenges solved using the Internet of Things leveraging the software-defined network. We provide a topical survey of the application and impact of software-defined networking on the Internet of things networks. We also study the impact of machine learning techniques applied to software-defined networking and its application perspective. The study is carried out from the different perspectives of software-based Internet of Things networks, including wide-area networks, edge networks, and access networks. Machine learning techniques are presented from the perspective of network resources management, security, classification of traffic, quality of experience, and quality of service prediction. Finally, we discuss challenges and issues in adopting machine learning and software-defined networking for the Internet of Things applications.

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Khoroshko, Leonid Leonidovich, Peter A. Ukhov, and Pavel P. Keyno. "Development of Massive Open Online Courses Based on 3D Computer Graphics and Multimedia." International Journal of Engineering Pedagogy (iJEP) 9, no. 4 (August 29, 2019): 4. http://dx.doi.org/10.3991/ijep.v9i4.10193.

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This work is devoted to the creation of a laboratory workshop (virtual) for open online courses based on programs of three-dimensional computer graphics and multimedia. The issues of using SolidWorks, Autodesk® 3DS MAX software in distance learning are discussed. The software was used to prepare training materials for the courses course "Machines and mechanisms theory", "Computer Graphics" and "Engineering and Computer Graphics". Using the software product SolidWorks, Autodesk® 3DS MAX has significantly increased the visibility of the course and develop tools for organizing the independent work of students in an interactive mode.

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Toth, Laszlo, and Laszlo Vidacs. "Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements." Information Technology And Control 48, no. 3 (September 24, 2019): 432–45. http://dx.doi.org/10.5755/j01.itc.48.3.21973.

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Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.

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Korzeniowski, Łukasz, and Krzysztof Goczyła. "Artificial intelligence for software development — the present and the challenges for the future." Bulletin of the Military University of Technology 68, no. 1 (March 29, 2019): 15–32. http://dx.doi.org/10.5604/01.3001.0013.1464.

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Since the time when first CASE (Computer-Aided Software Engineering) methods and tools were developed, little has been done in the area of automated creation of code. CASE tools support a software engineer in creation the system structure, in defining interfaces and relationships between software modules and, after the code has been written, in performing testing tasks on different levels of detail. Writing code is still the task of a skilled human, which makes the whole software development a costly and error-prone process. It seems that recent advances in AI area, particularly in deep learning methods, may considerably improve the matters. The paper presents an extensive survey of recent work and achievements in this area reported in the literature, both from the theoretical branch of research and from engineer-oriented approaches. Then, some challenges for the future work are proposed, classified into Full AI, Assisted AI and Supplementary AI research fields. Keywords: software development, artificial intelligence, machine learning, automated code generation

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Rasool, Raihan Ur, Usman Ashraf, Khandakar Ahmed, Hua Wang, Wajid Rafique, and Zahid Anwar. "Cyberpulse: A Machine Learning Based Link Flooding Attack Mitigation System for Software Defined Networks." IEEE Access 7 (2019): 34885–99. http://dx.doi.org/10.1109/access.2019.2904236.

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Jentzsch, Sophie, and Nico Hochgeschwender. "A qualitative study of Machine Learning practices and engineering challenges in Earth Observation." it - Information Technology 63, no. 4 (July 15, 2021): 235–47. http://dx.doi.org/10.1515/itit-2020-0045.

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Abstract Machine Learning (ML) is ubiquitously on the advance. Like many domains, Earth Observation (EO) also increasingly relies on ML applications, where ML methods are applied to process vast amounts of heterogeneous and continuous data streams to answer socially and environmentally relevant questions. However, developing such ML- based EO systems remains challenging: Development processes and employed workflows are often barely structured and poorly reported. The application of ML methods and techniques is considered to be opaque and the lack of transparency is contradictory to the responsible development of ML-based EO applications. To improve this situation a better understanding of the current practices and engineering-related challenges in developing ML-based EO applications is required. In this paper, we report observations from an exploratory study where five experts shared their view on ML engineering in semi-structured interviews. We analysed these interviews with coding techniques as often applied in the domain of empirical software engineering. The interviews provide informative insights into the practical development of ML applications and reveal several engineering challenges. In addition, interviewees participated in a novel workflow sketching task, which provided a tangible reflection of implicit processes. Overall, the results confirm a gap between theoretical conceptions and real practices in ML development even though workflows were sketched abstractly as textbook-like. The results pave the way for a large-scale investigation on requirements for ML engineering in EO.

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Moreb, Mohammed, Tareq Abed Mohammed, Oguz Bayat, and Oguz Ata. "Corrections to “A Novel Software Engineering Approach Toward Using Machine Learning for Improving the Efficiency of Health Systems“." IEEE Access 8 (2020): 136459. http://dx.doi.org/10.1109/access.2020.2986259.

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Marques, Carla Verônica Machado, Carlo Emmanoel Tolla de Oliveira, and Cibele Ribeiro da Cunha Oliveira. "The Cognitive Machine as Mental Language Automata." International Journal of Cognitive Informatics and Natural Intelligence 12, no. 1 (January 2018): 75–91. http://dx.doi.org/10.4018/ijcini.2018010106.

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This article describes how learning is a native ability of the brain. However, very little is known of the process as it happens. The engineering model presented in this work provides a base to explore the innards of cognition. The computational implementation of the model is usable to assess cognitive profiles by means of machine learning and harmonic filtering. The model relies on an evolutionary dimensional space consisting of phylogenetic, ontogenetic and microgenetic timelines. The microgenetic space reveals the state machine nature of cognition, standing as an internal translator to a brain specific language. The study of this machine and its language is the key to understanding cognition.

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Journal articles on the topic 'Computer software. Software engineering. Machine learning'

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