Enhancing Automated Analysis of Bug Descriptions and Report Generation Using Machine Learning & NLP

Authors

  • D. Aruna Assistant Professor, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Bommasani Lakshmi Prasanna UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Valisetti Bhagya Lakshmi UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Jannu Veera Phani Ganesh UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author
  • Abdul Neelshuk Asmith UG Student, Department of CSE-AI & ML, Sri Vasavi Institute of Engineering & Technology, Nandamuru, Andhra Pradesh, India Author

Keywords:

Summarization, Natural Language Processing, Machine Learning, Software Artifacts, Bug reports

Abstract

Bug reports can provide a great deal of assistance for developers during the process of development. But due to the large size of bug repositories, it is sometimes difficult to take advantage of these artifacts in the available time. One way of helping developers to provide summaries of these reports and provide relevant details only. Once it’s decided that this is the required report then one can study the details. As text mining technology advances, many substantial approaches have been proposed to generate optimized summaries for bug reports. In this project, we have proposed an extractive based methodology for the generation of summaries of bug reports by using the sentence embedding. We achieved improved rouge-1 and rouge2 results than the previous state of the art systems for the bug report summary generation.

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References

D. Cubrani c and G.C. Murphy, “Hipikat: Recommending Pertinent Software Development Artifacts,” Proc. 25th Int’l Conf. Software Eng. (ICSE ’03), pp. 408-418, 2003.

C. Sun, D. Lo, S.-C. Khoo, and J. Jiang, “Towards More Accurate Retrieval of Duplicate Bug Reports,” Proc. 26th Int’l Conf. Automated Software Eng. (ASE ’11), pp. 253-262, 2011.

A. Nenkova and K. McKeown, “Automatic Summarization,” Foundations and Trends in Information Retrieval, vol. 5, no. 2/3, pp. 103-233, 2011.

K. Zechner, “Automatic Summarization of Open-Domain Multiparty Dialogues in Diverse Genres,” Computational Linguistics, vol. 28, no. 4, pp. 447-485, 2002.

X. Zhu and G. Penn, “Summarization of Spontaneous Conversations,” Proc. Ninth Int’l Conf. Spoken Language Processing (Interspeech ’06- ICSLP), pp. 1531-1534, 2006.

O. Rambow, L. Shrestha, J. Chen, and C. Lauridsen,“Summarizing Email Threads,” Proc. Human Language Technology Conf. North Am. Chapter of the Assoc. for Computational Linguistics (HLT-NAACL ’04), 2004.

R.J. Sandusky and L. Gasser, “Negotiation and the Coordination of Information and Activity in Distributed Software Problem Management,” Proc. Int’l ACM SIGGROUP Conf. Supporting Group Work (GROUP ’05), pp. 187-196, 2005.

D. Bertram, A. Voida, S. Greenberg, and R. Walker, “Communication, Collaboration, and Bugs: The Social Nature of Issue Tracking in Small, Collocated Teams,” Proc. ACM Conf. Computer Supported Cooperative Work (CSCW ’10), pp. 291-300, 2010.

R. Lotufo, Z.Malik, andK. Czarnecki, “Modelling the ‘Hurried’ Bug Report Reading Process to Summarize Bug Reports,” Proc. IEEE 28th Int’l Conf. SoftwareMaintenance (ICSM’12).

S. Mani, R. Catherine, V.S. Sinha, and A. Dubey, “AUSUM: Approach for Unsupervised Bug Report Summarization,” Proc. ACM SIGSOFT 20th Int’l Symp. The Foundations of Software Eng. (FSE ’12), article 11, 2012.

S. Rastkar, G.C. Murphy, and G. Murray, “Summarizing Software Artifacts: A Case Study of Bug Reports,” Proc. 32nd ACM/IEEE International Conference on Software Engineering (ICSE ’10), pp.505–514, 2010.

S. Rastkar, G.C. Murphy, and G. Murray, “Automatic Summarization of Bug Reports,” IEEE Trans. Software Eng., vol.40, no.4, pp.366– 380, April 2014.

R. Lotufo, Z. Malik, and K. Czarnecki, “Modelling the ‘Hurried’ Bug Report Reading Process to Summarize Bug Reports,” Empir. Softw. Eng., vol.20, no.2, pp.516–548, April 2015.

H. Jiang, N. Nazar, J. Zhang, T. Zhang, and Z. Ren, “PRST: A PageRank-Based Summarization Technique for Summarizing Bug Reports with Duplicates,” Int. J. Softw. Eng. Know., vol.27, no.6, pp.869–896, 2017.

Xiaochen Li, He Jiang, Dong Liu, Zhilei Re, Ge Li (2018), “Unsupervised Deep Bug Report Summarization” ICPC '18 Proceedings of the 26th Conference on Program Comprehension, Pages 144-155.

https://github.com/summanlp/evaluation/tree/master/ROUGERELEASE-1.5.5#start-of-content

NLTK; Available from: https://www.nltk.org/.

Cheng-Zen YANG, Cheng-Min AO, Yu-Han CHUNG(2018), “Towards an Improvement of Bug Report Summarization Using TwoLayer Semantic Information”, IEICE Transactions on Information and Systems. VOL.E101–D, NO.7, Pages 1743-1750

S. Haiduc, J. Aponte, L. Moreno, and A. Marcus, “On the Use of Automated Text Summarization Techniques for Summarizing Source Code,” Proc. 17th Working Conf. Reverse Eng. (WCRE ’10), pp. 35- 44, 2010.

G. Sridhara, E. Hill, D. Muppaneni, L. Pollock, and K. Vijay- Shanker, “Towards Automatically Generating Summary Comments for Java Methods,” Proc. 25th Int’l Conf. Automated Software Eng. (ASE ’10), pp. 43-52, 2010.

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Published

26-04-2024

Issue

Section

Research Articles

How to Cite

Enhancing Automated Analysis of Bug Descriptions and Report Generation Using Machine Learning & NLP. (2024). International Journal of Scientific Research in Science and Technology, 11(2), 875-883. https://ijsrst.com/index.php/home/article/view/IJSRST24112149

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