Software bugs: detection, analysis and fixing

ABSTRACT

The focus of this study is on detecting, analyzing, and fixing of software bugs. The main goal is to identify cost-effective methods for developing and managing software systems by introducing a post-deployment debugging approach. This approach serves two purposes: (1) tracking software stability and (2) acting as a repository for software bug data. To achieve this objective, a web-based application called BugTracker was proposed and developed. BugTracker manages software testing and post-deployment activities while also serving as a repository for bug data. Testers and end-users can report bugs through BugTracker, which developers analyze and resolve by updating the program files with new versions. The BugTracker system was designed using UML and Overview models, and implemented using PHP, HTML, JavaScript, and MySQL database technology. Evaluation and testing of BugTracker demonstrated increased developer productivity, reduced production costs, and improved software stability. This study shows that effectively managing post-deployment activities can enhance software stability and reduce development costs.

References

• S. Alqahtani, J. Rilling, An ontology-based approach to automate tagging of software artifacts. In 2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM) (2017) (pp. 169-174).
• J. Lutz, J. F. Naveda, J. R. Vallino, Undergraduate software engineering. Communications of the ACM 57(8) (2014) 52-58
• Bian, B. Liang, W. Shi, J. Huang, Y. Cai, Nar-miner: discovering negative association rules from code for bug detection. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2018 October) (pp. 411-422).
• Budhiraja, K. Dutta, R. Reddy, M. Shrivastava, DWEN: deep word embedding network for duplicate bug report detection in software repositories. In Proceedings of the 40th International Conference on Software Engineering: Companion Proceedings (2018 May) (pp. 193-194).
• Z. Chen, H. X. Ding, J. Zhang, W. A. N. G. Yang, G. Zhang, Y. N. Wang, An artificial intelligence (AI) defect detection technology based on software behavior decision tree. 2018 International Conference on Computer, Communication and Network Technology (CCNT 2018) (pp. 113-126).
• Elmishali, R. Stern, M. Kalech, An artificial intelligence paradigm for troubleshooting software bugs. Engineering Applications of Artificial Intelligence 69 (2018) 147-156
• Elmishali, R. Stern, M. Kalech, Debguer: A tool for bug prediction and diagnosis. In Proceedings of the AAAI Conference on Artificial Intelligence 33 (01) 9446-9451
• C. Amusuo, A. Sharma, S. R. Rao, A. Vincent, J. C. Davis, Reflections on software failure analysis. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2022 November) (pp. 1615-1620).
• Hammouri, M. Hammad, M. Alnabhan, F. Alsarayrah, Software bug prediction using machine learning approach. International Journal of Advanced Computer Science and Applications 9(2) (2018).
• Hindle, C. Onuczko, Preventing duplicate bug reports by continuously querying bug reports. Empirical Software Engineering 24(2) (2019) 902–936
• Šmite, C. Wohlin, Z. Galviņa, R. Prikladnicki, An empirically based terminology and taxonomy for global software engineering. Empirical Software Engineering 19 (2014) 105-153
• Kumar, D. L. Gupta, Software bug prediction system using neural network. European Journal of Advances in Engineering and Technology 3(7) (2016) 78-84.
• Li, P. He, J. Zhu, M. R. Lyu, Software defect prediction via convolutional neural network. In 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS) (2017 July) (pp. 318-328).
• Li, J. Chen, Z. Lin, L. Zhang, Z. Wang, M. Zhou, W. Xie, A mining approach to obtain the software vulnerability characteristics. In 2017 fifth international conference on advanced cloud and big data (CBD) (2017 August) (pp. 296-301).
• Ma, Y. Liu, W. C. Lee, X. Zhang, A. Grama, MODE: automated neural network model debugging via state differential analysis and input selection. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (2018 October) (pp. 175-186).
• Manjula, L. Florence, Deep neural network based hybrid approach for software defect prediction using software metrics. Cluster Computing 22(4) (2019) 9847-9863.
• F. Otoom, D. Al-Shdaifat, M. Hammad, E. E. Abdallah, Severity prediction of software bugs. In 2016 7th International Conference on Information and Communication Systems (ICICS) (2016 April) (pp. 92-95).
• K. Pandey, R. B. Mishra, A. K. Tripathi, BPDET: An effective software bug prediction model using deep representation and ensemble learning techniques. Expert Systems with Applications 144 (2020) 113085
• K. Pandey, R. B. Mishra, A. K. Triphathi, Software bug prediction prototype using Bayesian network classifier: A comprehensive model. Procedia Computer Science 132 (2018) 1412-1421
• Ricca, P. Tonella, Analysis and testing of web applications. In IEEE Proceedings of the 23rd International Conference on Software Engineering ICSE 2001 (2001 May) (pp. 25-34).
• Schekotihin, P. Rodler, W. Schmid, M. Horridge, T. Tudorache, Test-driven ontology development in Protégé. In Proceedings of the 9th International Conference on Biological Ontology (ICBO 2018) Corvallis Oregon USA (pp. 1-6).
• Sheneamer, J. Kalita, A survey of software clone detection techniques. International Journal of Computer Applications 137(10) (2016) 1-21.
• Anthony, Writing research article introductions in software engineering: How accurate is a standard model?. IEEE Transactions on Professional Communication 42(1) (1999) 38-46
• Uddin, R. Ghazali, M. M. Deris, R. Naseem, H. Shah, A survey on bug prioritization. Artificial Intelligence Review 47(2) (2017) 145–180
• Kulkarni, A. Kolhe, J. Kulkarni, Intelligent software engineering: the significance of artificial intelligence techniques in enhancing software development lifecycle processes. In International Conference on Intelligent Systems Design and Applications Cham: Springer International Publishing (2021 December) (pp. 67-82).
• Xie, Intelligent software engineering: Synergy between AI and software engineering. In Proceedings of the 11th Innovations in Software Engineering Conference (2018) (pp. 1-1).
• Rodríguez-Pérez, G. Robles, A. Serebrenik, A. Zaidman, D. M. Germán, J. M. Gonzalez-Barahona, How bugs are born: a model to identify how bugs are introduced in software components. Empirical Software Engineering 25 (2020) 1294-1340
• W. Bello, O. M. Moradeyo, A. S. Olaniyan, Web Request Level Protection of Cyber Applications against Threats and Attacks. International Journal of Scientific Engineering and Science 2(1) (2018) 52-56
• Bader, A. Scott, M. Pradel, S. Chandra, Getafix: Learning to fix bugs automatically. Proceedings of the ACM on Programming Languages 3(OOPSLA) (2019) 1-27.
• Kim, T. Zimmermann, K. Pan, E. James Jr, Automatic identification of bug-introducing changes. In 21st IEEE/ACM International Conference on Automated Software Engineering (ASE’06) (2006 September) (pp. 81-90).
• W. Bello, F. N. Otobo, Stored-knowledge based troubleshooting and diagnosing system. International Journal of Scientific Engineering and Science 2(5) (2018) 19-24
• Ni, B. Li, X. Sun, T. Chen, B. Tang, X. Shi, Analyzing bug fix for automatic bug cause classification. Journal of Systems and Software 163 (2020) 110538.
• Liu, D. Kim, T. F. Bissyandé, S. Yoo, Y. Le Traon, Mining fix patterns for findbugs violations. IEEE Transactions on Software Engineering 47(1) (2018) 165-188.
• Perkusich, L. C. e Silva, A. Costa, F. Ramos, R. Saraiva, A. Freire, …, A. Perkusich, Intelligent software engineering in the context of agile software development: A systematic literature review. Information and Software Technology 119 (2020) 106241

Download all article in PDF

WSN 189 (2024) 1-22

ADVERTISEMENT