Enhanced Bug Localization through Version Tag Embedding: A Comprehensive Approach to Efficient Software Development

Authors

  • N. Rama Rao Associate Professor, School of Engineering, Department of CSE (AIML), Mallareddy University, Hyderabad
  • K. Suresh Associate Professor in School of Information Technology, JNTUH, India

Keywords:

Build and Release Management, Software Configuration Management, Embedding Version Tag, Integration, Software Release Management, Version Control System

Abstract

In order to localize bugs, this study suggests putting version markers in software delivery files. The article focuses on the importance and method of the planned build process, in which developers upgrade internal version numbers prior to registration to aid with precise problem localization. It is suggested that version tagging automation be used as a workable solution to problems like identifying file sources and exploiting vulnerabilities. The suggested solution has advantages in better managing project complexity, protecting against software infiltration, lowering costs, improving quality, and boosting productivity and dependability. The use of date tagging and external release numbers for software identification and compatibility testing is also covered in the article. Operating systems, version control, build tools, web servers, application servers, scripting languages, bug tracking tools, databases, and programming languages are all used in the research's specialised testing and development environment. The branching, merging, check-out, check-in, parameters, build numbering approach, and tagging operations are all thoroughly detailed. The suggested approach shows how to embed and update internal version tags in deliverable files, improving traceability and facilitating problem fixes throughout the build process. Discussions of the approach's importance in terms of bug detection and eradication, release management, complexity handling, intrusion defence, cost savings, and dependability are included. The page includes graphs that show the number of builds and the link between the number of builds and the state of problems that have been discovered and repaired before and after releases. Overall, the research offers workable solutions for effective software development and problem management by presenting a unique bug localization technique employing version tagging.

Downloads

Download data is not yet available.

References

Rao, N. R., & Sekharaiah, K. C. (2015). Embedding version tag in software file deliverables before build release. 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), 1–6. https://doi.org/10.1109/ICRITO.2015.7359255

Hamdy, A., & Arabi, A. E. (2022). Locating faulty source code files to fix bug reports: International Journal of Open Source Software and Processes, 13(1), 1–15. https://doi.org/10.4018/IJOSSP.308791

Liu, G., Lu, Y., Shi, K., Chang, J., & Wei, X. (2019). Mapping bug reports to relevant source code files based on the vector space model and word embedding. IEEE Access, 7, 78870–78881. https://doi.org/10.1109/ACCESS.2019.2922686

Bendix, L., Kojo, T., & Magnusson, J. (2011, August). Software configuration management issues with industrial opensourcing. In 2011 IEEE Sixth International Conference on Global Software Engineering Workshop (pp. 85-89). IEEE. https://doi.org/10.1109/ICGSE-W.2011.21

Neville-Neil, G. V. (2009). Kode viciousSystem changes and side effects. Communications of the ACM, 52(4), 25–26. https://doi.org/10.1145/1498765.1498777

Lekha Tummala,Hruthik Gavva,.Maanvitha Gona, Lakshmi Tulasi.P (2021).Virtual Controller: managing a remote computer using network communication. International Journal of Computer Engineering In Research Trends. 8(12), 216-219,

Walrad, C., & Strom, D. (2002). The importance of branching models in SCM. Computer, 35(9), 31–38. https://doi.org/10.1109/MC.2002.1033025.

P. Siva (2022). Prediction of Knee Osteoarthritis Using Deep Learning. International Journal of Computer Engineering in Research Trends. 8(12), 228-235.

Lai, R., Garg, M., Kapur, P. K., & Liu, S. (2011). A study of when to release a software product from the perspective of software reliability models. Journal of Software, 6(4), 651–661. https://doi.org/10.4304/jsw.6.4.651-661

Rao, N. R., & Sekharaiah, K. C. (2013). An incremental risk management framework for realizing project efficiency using version control. In CCSN and IJCA India 2013 (pp. 1-6), https://research.ijcaonline.org/ccsn2013/number3/ccsn1301.pdf.

Neely, S., & Stolt, S. (2013, August). Continuous delivery? easy! just change everything (well, maybe it is not that easy). In 2013 Agile Conference (pp. 121-128). IEEE, DOI: 10.1109/AGILE.2013.17.

Elbaz, M. (2011, August). To deliver faster, build it in reverse. In 2011 Agile Conference (pp. 230-233). IEEE, DOI: 10.1109/AGILE.2011.32.

Ostrand, T. J., Weyuker, E. J., & Bell, R. M. (2005). Predicting the location and number of faults in large software systems. IEEE Transactions on Software Engineering, 31(4), 340–355. https://doi.org/10.1109/TSE.2005.49

McIntosh, S., Adams, B., & Hassan, A. E. (2010, May). The evolution of ANT build systems. In 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) (pp. 42-51). IEEE, DOI: 10.1109/MSR.2010.5463341.

Huo, X., Li, M., & Zhou, Z. H. (2016, July). Learning unified features from natural and programming languages for locating buggy source code. In IJCAI (Vol. 16, pp. 1606-1612), http://129.211.169.156/publication/ijcai16npCNN.pdf.

Kim, D. Y., & Youn, C. (2010, June). Traceability Enhancement Technique through the integration of software configuration management and individual working environment. In 2010 Fourth International Conference on Secure Software Integration and Reliability Improvement (pp. 163-172). IEEE, DOI: 10.1109/SSIRI.2010.27.

Rudra Kumar, M., Pathak, R., Gunjan, V.K. (2022). Diagnosis and Medicine Prediction for COVID-19 Using Machine Learning Approach. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_10

Rudra Kumar, M., Pathak, R., Gunjan, V.K. (2022). Machine Learning-Based Project Resource Allocation Fitment Analysis System (ML-PRAFS). In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_1

Pingili, Madhavi & Sreenivasulu, K. & Maloth, Bhav Singh & Saheb, Shaik & Saleh, Alaa. (2022). Bug2 algorithm-based data fusion using mobile element for IoT-enabled wireless sensor networks. Measurement: Sensors. 24. 100548. 10.1016/j.measen.2022.100548.

M. M. Venkata Chalapathi, M. Rudra Kumar, Neeraj Sharma, S. Shitharth, "Ensemble Learning by High-Dimensional Acoustic Features for Emotion Recognition from Speech Audio Signal", Security and Communication Networks, vol. 2022, Article ID 8777026, 10 pages, 2022. https://doi.org/10.1155/2022/8777026

Ramana, Kadiyala, et al. "Leaf disease classification in smart agriculture using deep neural network architecture and IoT." Journal of Circuits, Systems and Computers 31.15 (2022): 2240004. https://doi.org/10.1142/S0218126622400047

Bugzilla main page. (n.d.). Retrieved 13 May 2023, from https://bugs.eclipse.org/bugs/

http://git.eclipse.org/, https://github.com/eclipse/.

Include or update the IVT to Deliverable file in the build procedure that has been recommended.

Downloads

Published

17.05.2023

How to Cite

Rao, N. R., & Suresh, K. . (2023). Enhanced Bug Localization through Version Tag Embedding: A Comprehensive Approach to Efficient Software Development. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 417–427. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2866

Issue

Section

Research Article