DigBug—pre/post-processing operator selection for accurate bug localization

Kim, Kisub and Ghatpande, Sankalp and Liu, Kui and Koyuncu, Anıl and Kim, Dongsun and Bissyande, Tegawende F. and Klein, Jacques and Le Traon, Yves (2022) DigBug—pre/post-processing operator selection for accurate bug localization. Journal of Systems and Software, 189 . ISSN 0164-1212 (Print) 1873-1228 (Online)

[thumbnail of 1-s2.0-S0164121222000528-main_(1).pdf] PDF
1-s2.0-S0164121222000528-main_(1).pdf
Restricted to Registered users only

Download (1MB) | Request a copy

Abstract

Bug localization is a recurrent maintenance task in software development. It aims at identifying relevant code locations (e.g., code files) that must be inspected to fix bugs. When such bugs are reported by users, the localization process become often overwhelming as it is mostly a manual task due to incomplete and informal information (written in natural languages) available in bug reports. The research community has then invested in automated approaches, notably using Information Retrieval techniques. Unfortunately, reported performance in the literature is still limited for practical usage. Our key observation, after empirically investigating a large dataset of bug reports as well as workflow and results of state-of-the-art approaches, is that most approaches attempt localization for every bug report without considering the different characteristics of the bug reports. We propose DigBug as a straightforward approach to specialized bug localization. This approach selects pre/post-processing operators based on the attributes of bug reports; and the bug localization model is parameterized in accordance as well. Our experiments confirm that departing from ‘‘one-size-fits-all’’ approaches, DigBug outperforms the state-of-the-art techniques by 6 and 14 percentage points, respectively in terms of MAP and MRR on average.
Item Type: Article
Uncontrolled Keywords: Bug report; Bug localization; Fault localization; Bug characteristics; Information retrieval; Operator combination
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Anıl Koyuncu
Date Deposited: 30 Mar 2022 12:06
Last Modified: 26 Apr 2022 10:29
URI: https://research.sabanciuniv.edu/id/eprint/42798

Actions (login required)

View Item
View Item