@inproceedings{baeb2b3f98d64314b944520129edbf8a,
title = "Early Identification of Invalid Bug Reports in Industrial Settings – A Case Study",
abstract = "Software development companies spend considerable time resolving bug reports. However, bug reports might be invalid, i.e., not point to a valid flaw. Expensive resources and time might be expended on invalid bug reports before discovering that they are invalid. In this case study, we explore the impact of invalid bug reports and develop and assess the use of machine learning (ML) to indicate whether a bug report is likely invalid. We found that about 15% of bug reports at the case company are invalid, and that their resolution time is similar to valid bug reports. Among the ML-based techniques we used, logistic regression and SVM show promising results. In the feedback, practitioners indicated an interest in using the tool to identify invalid bug reports at early stages. However, they emphasized the need to improve the explainability of ML-based recommendations and to reduce the maintenance cost of the tool.",
keywords = "Bug classification, Bug reports, Invalid bugs, Machine learning, Software analytics, Valid bugs",
author = "Muhammad Laiq and Ali, {Nauman bin} and J{\"u}rgen B{\"o}stler and Emelie Engstr{\"o}m",
year = "2022",
doi = "10.1007/978-3-031-21388-5_34",
language = "English",
isbn = "9783031213878",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media B.V.",
pages = "497--507",
editor = "Davide Taibi and Marco Kuhrmann and Tommi Mikkonen and Pekka Abrahamsson and Jil Kl{\"u}nder",
booktitle = "Product-Focused Software Process Improvement - 23rd International Conference, PROFES 2022, Proceedings",
address = "United States",
note = "23rd International Conference on Product-Focused Software Process Improvement, PROFES 2022 ; Conference date: 21-11-2022 Through 23-11-2022",
}