Littérature scientifique sur le sujet « SOFTWARE FAULT PRONENESS »
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Articles de revues sur le sujet "SOFTWARE FAULT PRONENESS"
Denaro, Giovanni, Mauro Pezzè et Sandro Morasca. « Towards Industrially Relevant Fault-Proneness Models ». International Journal of Software Engineering and Knowledge Engineering 13, no 04 (août 2003) : 395–417. http://dx.doi.org/10.1142/s0218194003001366.
Texte intégralGatrell, Matt, et 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 (avril 2012) : 69–88. http://dx.doi.org/10.4018/jismd.2012040103.
Texte intégralBhandari, Guru Prasad, Ratneshwer Gupta et Satyanshu Kumar Upadhyay. « An approach for fault prediction in SOA-based systems using machine learning techniques ». Data Technologies and Applications 53, no 4 (3 septembre 2019) : 397–421. http://dx.doi.org/10.1108/dta-03-2019-0040.
Texte intégralShatnawi, Raed, et 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 (janvier 2021) : 62–78. http://dx.doi.org/10.4018/ijitsa.2021010104.
Texte intégralSingh, Rajvir, Anita Singhrova et 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 (juillet 2018) : 15–35. http://dx.doi.org/10.4018/ijossp.2018070102.
Texte intégralGondra, Iker. « Applying machine learning to software fault-proneness prediction ». Journal of Systems and Software 81, no 2 (février 2008) : 186–95. http://dx.doi.org/10.1016/j.jss.2007.05.035.
Texte intégralShatnawi, Raed. « Software fault prediction using machine learning techniques with metric thresholds ». International Journal of Knowledge-based and Intelligent Engineering Systems 25, no 2 (26 juillet 2021) : 159–72. http://dx.doi.org/10.3233/kes-210061.
Texte intégralKhanna, Munish, Abhishek Toofani, Siddharth Bansal et Mohammad Asif. « Performance Comparison of Various Algorithms During Software Fault Prediction ». International Journal of Grid and High Performance Computing 13, no 2 (avril 2021) : 70–94. http://dx.doi.org/10.4018/ijghpc.2021040105.
Texte intégralJ. Pai, Ganesh, et Joanne Bechta Dugan. « Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods ». IEEE Transactions on Software Engineering 33, no 10 (octobre 2007) : 675–86. http://dx.doi.org/10.1109/tse.2007.70722.
Texte intégralGatrell, Matt, et 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.
Texte intégralThèses sur le sujet "SOFTWARE FAULT PRONENESS"
Abdilrahim, Ahmad, et 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.
Texte intégralDuc, 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.
Texte intégralDeniz, 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.
Texte intégrals leading defense industry company: Aselsan&rsquo
s software engineering department. We aimed to explore their real-life software projects and interpret reuse and quality relations for their projects. With this intention, we defined four different hypotheses to determine reuse and quality relations
and in order to confirm these hypotheses
we designed three separate case studies. In these case studies, we collected and calculated reuse and quality metrics i.e. Object-oriented quality metrics, reuse rates and performance measures of individual modules, fault-proneness of software components, and productivity rates of different products. Finally, by analyzing these measurements, we developed suggestions to further benefit from reuse in Aselsan through systematic improvements to the reuse infrastructure and process. Similar case studies have been reported in the literature, however, in Turkey, there are not many case studies using real-life project data, particularly in the defense industry.
BANSAL, ANKITA. « DEVELOPMENT OF TECHNIQUES AND MODELS FOR IMPROVING SOFTWARE QUALITY ». Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14692.
Texte intégralBANSAL, 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.
Texte intégralJaafar, Fehmi. « Analysing artefacts dependencies to evolving software systems ». Thèse, 2013. http://hdl.handle.net/1866/10514.
Texte intégralProgram maintenance accounts for the largest part of the costs of any program. During maintenance activities, developers implement changes (sometimes simultaneously) on artefacts to fix bugs and to implement new requirements. Thus, developers need knowledge to identify hidden dependencies among programs artefacts and detect correlated artefacts. As programs evolved, their designs become more complex over time and harder to change. In the absence of the necessary knowledge on artefacts dependencies, developers could introduce design defects and faults that causes development and maintenance costs to rise. Therefore, developers must understand the dependencies among program artefacts and take proactive steps to facilitate future changes and minimize fault proneness. On the one hand, maintaining a program without understanding the different dependencies between their artefacts may lead to the introduction of faults. On the other hand, when developers lack knowledge about the impact of their maintenance activities, they may introduce design defects, which have a negative impact on program evolution. Thus, developers need mechanisms to understand how a change to an artefact will impact the rest of the programs artefacts and tools to detect design defects impact. In this thesis, we propose three principal contributions. The first contribution is two novel change patterns to model new co-change and change propagation scenarios. We introduce the Asynchrony change pattern, corresponding to macro co-changes, i.e., of files that co-change within a large time interval (change periods), and the Dephase change pattern, corresponding to dephase macro co-changes, i.e., macro co-changes that always happen with the same shifts in time. We present our approach, named Macocha, and we show that such new change patterns provide interesting information to developers. The second contribution is proposing a novel approach to analyse the evolution of different classes in object-oriented programs and to link different evolution behaviour to faults. In particular, we define an evolution model for each class to study the evolution and the co-evolution dependencies among classes and to relate such dependencies with fault-proneness. The third contribution concerns design defect dependencies impact. We propose a study to mine the link between design defect dependencies, such as co-change dependencies and static relationships, and fault proneness. We found that the negative impact of design defects propagate through their dependencies. The three contributions are evaluated on open-source programs.
Chapitres de livres sur le sujet "SOFTWARE FAULT PRONENESS"
Singh, Yogesh, Arvinder Kaur et Ruchika Malhotra. « Predicting Software Fault Proneness Model Using Neural Network ». Dans Lecture Notes in Business Information Processing, 215–17. Berlin, Heidelberg : Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68255-4_26.
Texte intégralLuo, Yunfeng, Kerong Ben et Lei Mi. « Software Metrics Reduction for Fault-Proneness Prediction of Software Modules ». Dans Lecture Notes in Computer Science, 432–41. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15672-4_36.
Texte intégralOstrand, Thomas J., et Elaine J. Weyuker. « Can File Level Characteristics Help Identify System Level Fault-Proneness ? » Dans Hardware and Software : Verification and Testing, 176–89. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34188-5_16.
Texte intégralSharma, Pooja, et Amrit Lal Sangal. « Soft Computing Approaches to Investigate Software Fault Proneness in Agile Software Development Environment ». Dans Algorithms for Intelligent Systems, 217–33. Singapore : Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3357-0_15.
Texte intégralTakagi, Tomohiko, et Mutlu Beyazıt. « Optimized Test Case Generation Based on Operational Profiles with Fault-Proneness Information ». Dans Software Engineering Research, Management and Applications, 15–25. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11265-7_2.
Texte intégralDalal, Renu, Manju Khari et Dimple Chandra. « Evaluation of Software Fault Proneness with a Support Vector Machine and Biomedical Applications ». Dans Bioelectronics and Medical Devices, 77–103. Boca Raton : Apple Academic Press, 2021. http://dx.doi.org/10.1201/9781003054405-4.
Texte intégralSingh, Rajvir, Anita Singhrova et Rajesh Bhatia. « Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software ». Dans Research Anthology on Usage and Development of Open Source Software, 596–618. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9158-1.ch032.
Texte intégralMalhotra, LinRuchika, et Ankita Jain Bansal. « Prediction of Change-Prone Classes Using Machine Learning and Statistical Techniques ». Dans Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability, 193–202. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4490-8.ch019.
Texte intégralMala, D. Jeya. « Investigating the Effect of Sensitivity and Severity Analysis on Fault Proneness in Open Source Software ». Dans Research Anthology on Recent Trends, Tools, and Implications of Computer Programming, 1743–69. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch078.
Texte intégralActes de conférences sur le sujet "SOFTWARE FAULT PRONENESS"
Destefanis, Giuseppe, Roberto Tonelli, Ewan Tempero, Giulio Concas et Michele Marchesi. « Micro Pattern Fault-Proneness ». Dans 2012 38th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2012. http://dx.doi.org/10.1109/seaa.2012.63.
Texte intégralDenaro, Giovanni, Sandro Morasca et Mauro Pezzè. « Deriving models of software fault-proneness ». Dans the 14th international conference. New York, New York, USA : ACM Press, 2002. http://dx.doi.org/10.1145/568760.568824.
Texte intégralJaafar, Fehmi, Foutse Khomh, Yann-Gael Gueheneuc et Mohammad Zulkernine. « Anti-pattern Mutations and Fault-proneness ». Dans 2014 14th International Conference on Quality Software (QSIC). IEEE, 2014. http://dx.doi.org/10.1109/qsic.2014.45.
Texte intégralDenaro, Giovanni. « Estimating software fault-proneness for tuning testing activities ». Dans the 22nd international conference. New York, New York, USA : ACM Press, 2000. http://dx.doi.org/10.1145/337180.337592.
Texte intégralHamid, Bushra, Eisa bin Abdullah Aleissa et Abdul Rauf. « Anticipating Software Fault Proneness using Classifier Ensemble : An Optimize Approach ». Dans Software Engineering. Calgary,AB,Canada : ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.780-021.
Texte intégralAfzal, Wasif. « Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness ». Dans 2010 17th Asia Pacific Software Engineering Conference (APSEC). IEEE, 2010. http://dx.doi.org/10.1109/apsec.2010.54.
Texte intégralHata, Hideaki, Osamu Mizuno et Tohru Kikuno. « Comparative Study of Fault-Proneness Filtering with PMD ». Dans 2008 IEEE International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2008. http://dx.doi.org/10.1109/issre.2008.49.
Texte intégralSeliya, N., T. M. Khoshgoftaar et S. Zhong. « Analyzing software quality with limited fault-proneness defect data ». Dans Ninth IEEE International Symposium on High-Assurance Systems Engineering. IEEE, 2005. http://dx.doi.org/10.1109/hase.2005.4.
Texte intégralMorasca, Sandro, et Luigi Lavazza. « Slope-based fault-proneness thresholds for software engineering measures ». Dans EASE '16 : 20th International Conference on Evaluation and Assessment in Software Engineering. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2915970.2915997.
Texte intégralLuo Yunfeng et Ben Kerong. « Metrics selection for fault-proneness prediction of software modules ». Dans 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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