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Relevant bibliographies by topics / SOFTWARE FAULT PRONENESS

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Academic literature on the topic 'SOFTWARE FAULT PRONENESS'

Author: Grafiati

Published: 11 September 2023

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Journal articles on the topic "SOFTWARE FAULT PRONENESS"

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Denaro, Giovanni, Mauro Pezzè, and Sandro Morasca. "Towards Industrially Relevant Fault-Proneness Models." International Journal of Software Engineering and Knowledge Engineering 13, no. 04 (2003): 395–417. http://dx.doi.org/10.1142/s0218194003001366.

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Estimating software fault-proneness early, i.e., predicting the probability of software modules to be faulty, can help in reducing costs and increasing effectiveness of software analysis and testing. The many available static metrics provide important information, but none of them can be deterministically related to software fault-proneness. Fault-proneness models seem to be an interesting alternative, but the work on these is still biased by lack of experimental validation. This paper discusses barriers and problems in using software fault-proneness in industrial environments, proposes a meth

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Gatrell, Matt, and Steve Counsell. "Faults and Their Relationship to Implemented Patterns, Coupling and Cohesion in Commercial C# Software." International Journal of Information System Modeling and Design 3, no. 2 (2012): 69–88. http://dx.doi.org/10.4018/jismd.2012040103.

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This paper documents a study of fault proneness in commercial, proprietary software and attempts to determine whether a relationship exists between class faults and the design context of a class, namely the coupling and cohesion of a class, and whether the class is a participant of common design patterns. The authors studied a commercial software system for a 24 month period and identified design pattern participants by inspecting the design documentation and source code; coupling and cohesion metrics were measured by inspecting the source code with a tool; we also extracted fault data for the

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Bhandari, Guru Prasad, Ratneshwer Gupta, and Satyanshu Kumar Upadhyay. "An approach for fault prediction in SOA-based systems using machine learning techniques." Data Technologies and Applications 53, no. 4 (2019): 397–421. http://dx.doi.org/10.1108/dta-03-2019-0040.

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Purpose Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML

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Shatnawi, Raed, and Alok Mishra. "An Empirical Study on Software Fault Prediction Using Product and Process Metrics." International Journal of Information Technologies and Systems Approach 14, no. 1 (2021): 62–78. http://dx.doi.org/10.4018/ijitsa.2021010104.

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Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-prone

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Singh, Rajvir, Anita Singhrova, and Rajesh Bhatia. "Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software." International Journal of Open Source Software and Processes 9, no. 3 (2018): 15–35. http://dx.doi.org/10.4018/ijossp.2018070102.

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Detection of fault proneness classes helps software testers to generate effective class level test cases. In this article, a novel technique is presented for an optimized test case generation for ant-1.7 open source software. Class level object oriented (OO) metrics are considered as effective means to find fault proneness classes. The open source software ant-1.7 is considered for the evaluation of proposed techniques as a case study. The proposed mathematical model is the first of its kind generated using Weka open source software to select effective OO metrics. Effective and ineffective OO

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Gondra, Iker. "Applying machine learning to software fault-proneness prediction." Journal of Systems and Software 81, no. 2 (2008): 186–95. http://dx.doi.org/10.1016/j.jss.2007.05.035.

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Shatnawi, Raed. "Software fault prediction using machine learning techniques with metric thresholds." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 2 (2021): 159–72. http://dx.doi.org/10.3233/kes-210061.

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BACKGROUND: Fault data is vital to predicting the fault-proneness in large systems. Predicting faulty classes helps in allocating the appropriate testing resources for future releases. However, current fault data face challenges such as unlabeled instances and data imbalance. These challenges degrade the performance of the prediction models. Data imbalance happens because the majority of classes are labeled as not faulty whereas the minority of classes are labeled as faulty. AIM: The research proposes to improve fault prediction using software metrics in combination with threshold values. Stat

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Khanna, Munish, Abhishek Toofani, Siddharth Bansal, and Mohammad Asif. "Performance Comparison of Various Algorithms During Software Fault Prediction." International Journal of Grid and High Performance Computing 13, no. 2 (2021): 70–94. http://dx.doi.org/10.4018/ijghpc.2021040105.

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Producing software of high quality is challenging in view of the large volume, size, and complexity of the developed software. Checking the software for faults in the early phases helps to bring down testing resources. This empirical study explores the performance of different machine learning model, fuzzy logic algorithms against the problem of predicting software fault proneness. The work experiments on the public domain KC1 NASA data set. Performance of different methods of fault prediction is evaluated using parameters such as receiver characteristics (ROC) analysis and RMS (root mean squa

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J. Pai, Ganesh, and Joanne Bechta Dugan. "Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods." IEEE Transactions on Software Engineering 33, no. 10 (2007): 675–86. http://dx.doi.org/10.1109/tse.2007.70722.

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Gatrell, Matt, and Steve Counsell. "Size, Inheritance, Change and Fault-proneness in C# software." Journal of Object Technology 9, no. 5 (2010): 29. http://dx.doi.org/10.5381/jot.2010.9.5.a2.

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Dissertations / Theses on the topic "SOFTWARE FAULT PRONENESS"

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Abdilrahim, Ahmad, and Caesar Alhawi. "Studying the Relation BetweenChange- and Fault-proneness : Are Change-prone Classes MoreFault-prone, and Vice-versa?" Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-97168.

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Software is the heartbeat of modern technology. To keep up with the new demands and expansion of requirements, changes are constantly introduced to the software, i.e., changes can also be made to fix an existing fault/defect. However, these changes might also cause further faults/defects in the software. This study aims to investigate the possible correlation between change-proneness and fault-proneness in object- oriented systems. Forty releases of five different open-source systems are analysed to quantify change- and fault-proneness; Beam, Camel, Ignite, Jenkins, and JMe- ter, then statisti

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Duc, Anh Nguyen. "The impact of design complexity on software cost and quality." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5708.

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Context: Early prediction of software cost and quality is important for better software planning and controlling. In early development phases, design complexity metrics are considered as useful indicators of software testing effort and some quality attributes. Although many studies investigate the relationship between design complexity and cost and quality, it is unclear what we have learned from these studies, because no systematic synthesis exists to date. Aim: The research presented in this thesis is intended to contribute for the body of knowledge about cost and quality prediction. A major

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Deniz, Berkhan. "Investigation Of The Effects Of Reuse On Software Quality In An Industrial Setting." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615318/index.pdf.

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Software reuse is a powerful tool in order to reduce development and maintenance time and cost. Any software life cycle product can be reused, not only fragments of source code. A high degree of reuse correlates with a low defect density. In the literature, many theoretical and empirical researches have examined the relationship of software reuse and quality. In this thesis, the effects of reuse on software quality are investigated in an industrial setting. Throughout this study, we worked with Turkey&rsquo<br>s leading defense industry company: Aselsan&rsquo<br>s software engineering departme

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BANSAL, ANKITA. "DEVELOPMENT OF TECHNIQUES AND MODELS FOR IMPROVING SOFTWARE QUALITY." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14692.

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ABSTRACT Prediction of quality attributes to improve software quality is gaining significant importance in the research. A number of metrics measuring important aspects of an object oriented program such as coupling, cohesion, inheritance and polymorphism have been proposed in the literature. Using these metrics, the quality attributes such as maintainability, fault proneness, change proneness, reliability etc. can be predicted during the early phases of the software development life cycle. Various models establishing the relationship between software metrics and quality attributes can

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BANSAL, ANJALI. "COMPARATIVE ANALYSIS OF CLASSIFICATION AND ENSEMBLE METHODS FOR PREDICTING SOFTWARE FAULT PRONENESS USING PROCESS METRICS." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18929.

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Various researchers have worked in the subject of software defect prediction to group the modules into defective or non-defective classes. But most of the previous studies done in this field utilize static code metrics to find the predicted value. The principal motive of this study is to evaluate the impact of process metrics on fault prediction performance using various classification techniques and ensemble techniques. In this study, we have analyzed the prediction performance of several classification and ensemble techniques based on three models: models that solely contain proc

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Jaafar, Fehmi. "Analysing artefacts dependencies to evolving software systems." Thèse, 2013. http://hdl.handle.net/1866/10514.

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Les logiciels sont en constante évolution, nécessitant une maintenance et un développement continus. Ils subissent des changements tout au long de leur vie, que ce soit pendant l'ajout de nouvelles fonctionnalités ou la correction de bogues. Lorsque les logiciels évoluent, leurs architectures ont tendance à se dégrader et deviennent moins adaptables aux nouvelles spécifications des utilisateurs. En effet, les architectures de ces logiciels deviennent plus complexes et plus difficiles à maintenir à cause des nombreuses dépendances entre les artefacts. Par conséquent, les développeurs doivent c

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Book chapters on the topic "SOFTWARE FAULT PRONENESS"

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Singh, Yogesh, Arvinder Kaur, and Ruchika Malhotra. "Predicting Software Fault Proneness Model Using Neural Network." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68255-4_26.

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Luo, Yunfeng, Kerong Ben, and Lei Mi. "Software Metrics Reduction for Fault-Proneness Prediction of Software Modules." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15672-4_36.

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Ostrand, Thomas J., and Elaine J. Weyuker. "Can File Level Characteristics Help Identify System Level Fault-Proneness?" In Hardware and Software: Verification and Testing. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34188-5_16.

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Sharma, Pooja, and Amrit Lal Sangal. "Soft Computing Approaches to Investigate Software Fault Proneness in Agile Software Development Environment." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3357-0_15.

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Takagi, Tomohiko, and Mutlu Beyazıt. "Optimized Test Case Generation Based on Operational Profiles with Fault-Proneness Information." In Software Engineering Research, Management and Applications. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11265-7_2.

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Dalal, Renu, Manju Khari, and Dimple Chandra. "Evaluation of Software Fault Proneness with a Support Vector Machine and Biomedical Applications." In Bioelectronics and Medical Devices. Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003054405-4.

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Singh, Rajvir, Anita Singhrova, and Rajesh Bhatia. "Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software." In Research Anthology on Usage and Development of Open Source Software. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9158-1.ch032.

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Detection of fault proneness classes helps software testers to generate effective class level test cases. In this article, a novel technique is presented for an optimized test case generation for ant-1.7 open source software. Class level object oriented (OO) metrics are considered as effective means to find fault proneness classes. The open source software ant-1.7 is considered for the evaluation of proposed techniques as a case study. The proposed mathematical model is the first of its kind generated using Weka open source software to select effective OO metrics. Effective and ineffective OO metrics are identified using feature selection techniques for generating test cases to cover fault proneness classes. In this methodology, only effective metrics are considered for assigning weights to test paths. The results indicate that the proposed methodology is effective and efficient as the average fault exposition potential of generated test cases is 90.16% and test cases execution time saving is 45.11%.

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Malhotra, LinRuchika, and Ankita Jain Bansal. "Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques." In Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4490-8.ch019.

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For software development, availability of resources is limited, thereby necessitating efficient and effective utilization of resources. This can be achieved through prediction of key attributes, which affect software quality such as fault proneness, change proneness, effort, maintainability, etc. The primary aim of this chapter is to investigate the relationship between object-oriented metrics and change proneness. Predicting the classes that are prone to changes can help in maintenance and testing. Developers can focus on the classes that are more change prone by appropriately allocating resources. This will help in reducing costs associated with software maintenance activities. The authors have constructed models to predict change proneness using various machine-learning methods and one statistical method. They have evaluated and compared the performance of these methods. The proposed models are validated using open source software, Frinika, and the results are evaluated using Receiver Operating Characteristic (ROC) analysis. The study shows that machine-learning methods are more efficient than regression techniques. Among the machine-learning methods, boosting technique (i.e. Logitboost) outperformed all the other models. Thus, the authors conclude that the developed models can be used to predict the change proneness of classes, leading to improved software quality.

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Mala, D. Jeya. "Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch078.

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Fault prone components in open source software leads to huge loss and inadvertent effects if not properly identified and rigorously tested. Most of the reported studies in the literature have applied design metrics alone, to identify such critical components. But in reality, some of the components' criticality level can be identified only by means of dynamic code analysis; as some of the components seem to be normal but still have higher level of impact on the other components. This leads to an insight on the need of a rigorous analysis based on how sensitive a component is and how severe will be the impact of it on other components in the system. To achieve this, an efficient mechanism of evaluating the criticality index of each component by means of sensitivity and severity analysis using the static design metrics and dynamic source code metrics has been proposed. Then, testing is conducted rigorously on these components using both unit testing and pair-wise integration testing.

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Conference papers on the topic "SOFTWARE FAULT PRONENESS"

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Destefanis, Giuseppe, Roberto Tonelli, Ewan Tempero, Giulio Concas, and Michele Marchesi. "Micro Pattern Fault-Proneness." In 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2012. http://dx.doi.org/10.1109/seaa.2012.63.

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Denaro, Giovanni, Sandro Morasca, and Mauro Pezzè. "Deriving models of software fault-proneness." In the 14th international conference. ACM Press, 2002. http://dx.doi.org/10.1145/568760.568824.

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Jaafar, Fehmi, Foutse Khomh, Yann-Gael Gueheneuc, and Mohammad Zulkernine. "Anti-pattern Mutations and Fault-proneness." In 2014 14th International Conference on Quality Software (QSIC). IEEE, 2014. http://dx.doi.org/10.1109/qsic.2014.45.

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Denaro, Giovanni. "Estimating software fault-proneness for tuning testing activities." In the 22nd international conference. ACM Press, 2000. http://dx.doi.org/10.1145/337180.337592.

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Hamid, Bushra, Eisa bin Abdullah Aleissa, and Abdul Rauf. "Anticipating Software Fault Proneness using Classifier Ensemble: An Optimize Approach." In Software Engineering. ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.780-021.

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Afzal, Wasif. "Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness." In 2010 17th Asia Pacific Software Engineering Conference (APSEC). IEEE, 2010. http://dx.doi.org/10.1109/apsec.2010.54.

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Hata, Hideaki, Osamu Mizuno, and Tohru Kikuno. "Comparative Study of Fault-Proneness Filtering with PMD." In 2008 IEEE International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2008. http://dx.doi.org/10.1109/issre.2008.49.

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Seliya, N., T. M. Khoshgoftaar, and S. Zhong. "Analyzing software quality with limited fault-proneness defect data." In Ninth IEEE International Symposium on High-Assurance Systems Engineering. IEEE, 2005. http://dx.doi.org/10.1109/hase.2005.4.

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Morasca, Sandro, and Luigi Lavazza. "Slope-based fault-proneness thresholds for software engineering measures." In EASE '16: 20th International Conference on Evaluation and Assessment in Software Engineering. ACM, 2016. http://dx.doi.org/10.1145/2915970.2915997.

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Luo Yunfeng and Ben Kerong. "Metrics selection for fault-proneness prediction of software modules." In 2010 International Conference on Computer Design and Applications (ICCDA 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccda.2010.5541206.

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