An Analysis of Improving Bug Fixing in Software Development

Daniel Caliman, Valentina David, Alexandra Băicoianu

2023

Abstract

When a defect arises during a software development process, architects and programmers spend significant time trying to identify whether any similar defects were identified during past assignments. To efficiently address a software issue, the developer must understand the context within which a software defect is reproducible and how it manifests itself. Another important aspect is how many other issues related to the same functionality were reported in the past and how they were solved. The current approach suggests using unsupervised machine learning models for natural language processing to identify past defects similar to the textual content of the newly reported defects. One of this study’s main benefits is ensuring a valuable knowledge transfer process that reduces the average time spent on bug fixing and better task distribution across team members. The innovative aspect of this research is gaining an increased ability to automate specific steps required for solving software reports.

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Paper Citation


in Harvard Style

Caliman D., David V. and Băicoianu A. (2023). An Analysis of Improving Bug Fixing in Software Development. In Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT; ISBN 978-989-758-665-1, SciTePress, pages 470-477. DOI: 10.5220/0012119500003538


in Bibtex Style

@conference{icsoft23,
author={Daniel Caliman and Valentina David and Alexandra Băicoianu},
title={An Analysis of Improving Bug Fixing in Software Development},
booktitle={Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT},
year={2023},
pages={470-477},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012119500003538},
isbn={978-989-758-665-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Software Technologies - Volume 1: ICSOFT
TI - An Analysis of Improving Bug Fixing in Software Development
SN - 978-989-758-665-1
AU - Caliman D.
AU - David V.
AU - Băicoianu A.
PY - 2023
SP - 470
EP - 477
DO - 10.5220/0012119500003538
PB - SciTePress