Bug Localization Based on Code Change Histories and Bug Reports

Youm, Klaus Changsun; Ahn, June; Kim, Jeongho; Lee, Eunseok

doi:10.1109/apsec.2015.23

Cited by 45 publications

(38 citation statements)

References 19 publications

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“…Previous Information Retrieval (IR)-based fault localization technologies such as BugScout [3], BugLocator [4], BLUiR [5], and BLIA [6] tend to utilize issue reports to search the potential faulty source files or classes. However, these approaches focus on desktop software, which is different with mobile apps.…”

Section: Introductionmentioning

confidence: 99%

Where2Change: Change Request Localization for App Reviews

Zhang

Chen

Zhan

et al. 2021

IIEEE Trans. Software Eng.

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Million of mobile apps have been released to the market. Developers need to maintain these apps so that they can continue to benefit end users. Developers usually extract useful information from user reviews to maintain and evolve mobile apps. One of the important activities that developers need to do while reading user reviews is to locate the source code related to requested changes. Unfortunately, this manual work is costly and time consuming since: (1) an app can receive thousands of reviews, and (2) a mobile app can consist of hundreds of source code files. To address this challenge, Palomba et al. recently proposed CHANGEADVISOR that utilizes user reviews to locate source code to be changed. However, we find that it cannot identify real source code to be changed for part of reviews. In this work, we aim to advance Palomba et al.'s work by proposing a novel approach that can achieve higher accuracy in change localization. Our approach first extracts the informative sentences (i.e., user feedback) from user reviews and identifies user feedback related to various problems and feature requests, and then cluster the corresponding user feedback into groups. Each group reports the similar users' needs. Next, these groups are mapped to issue reports by using W ord2V ec. The resultant enriched text consisting of user feedback and their corresponding issue reports is used to identify source code classes that should be changed by using our novel weight selection-based cosine similarity metric. We have evaluated the new proposed change request localization approach (Where2Change) on 31,597 user reviews and 3,272 issue reports of 10 open source mobile apps. The experiments demonstrate that Where2Change can successfully locate more source code classes related to the change requests for more user feedback clusters than CHANGEADVISOR as demonstrated by higher Top-N and Recall values. The differences reach up to 17 for Top-1, 18.1 for Top-3, 17.9 for Top-5, and 50.08% for Recall. In addition, we also compare the performance of Where2Change and two previous Information Retrieval (IR)-based fault localization technologies:BLUiR and BLIA. The results showed that our approach performs better than them. As an important part of our work, we conduct an empirical study to investigate the value of using both user reviews and historical issue reports for change request localization; the results shown that historical issue reports can help to improve the performance of change localization.

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Section: Introductionmentioning

confidence: 99%

Where2Change: Change Request Localization for App Reviews

Zhang

Chen

Zhan

et al. 2021

IIEEE Trans. Software Eng.

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“…We catered for this by comparing the search performance of ConCodeSe like for like (i.e., using the same datasets and the same criteria) with eight existing bug localisation approaches (Zhou et al 2012;Saha et al 2013;Moreno et al 2014;Wong et al 2014;Ye et al 2014;Youm et al 2015;Rahman et al 2015) as well as assessing the contribution of the off-the-shelf Lucene library's VSM. Therefore, the improvement in results can only be due to our approach.…”

Section: Threats To Validitymentioning

confidence: 99%

“…Our simpler approach only uses the first of Ye et al (2014) six features, lexical similarity, and yet provides better results on Tomcat, as we'll show. Youm et al (2015) introduced an approach where the scoring methods utilised in previous studies (Zhou et al 2012;Saha et al 2013;Wong et al 2014;) are first calculated individually and then combined together by varying alpha and beta parameter values. The approach, implemented in a tool called BLIA, is compared against the performance of the other tools where the original methods were first introduced.…”

Section: Combining Multiple Information Sourcesmentioning

confidence: 99%

“…Current state-of-the-art approaches for Java programs [BugLocator (Zhou et al 2012), BRTracer (Wong et al 2014), BLUiR (Saha et al 2013), AmaLgam , LearnToRank (Ye et al 2014), BLIA (Youm et al 2015) and Rahman et al (2015)] rely on project history to improve the suggestion of relevant source files. In particular they use similar BRs and recently modified files.…”

Section: Our Aim and Contributionsmentioning

confidence: 99%

“…SWT is a subproject of Eclipse and ZXing is an Android project maintained by Google. Saha et al (2013), Wong et al (2014), and Youm et al (2015) Eclipse 12863 For two of these projects w was set to 0.2 and for the other two w = 0.3. The performance of BugLocator was evaluated with 5 metrics: Top-1, Top-5, Top-10, MAP (mean average precision) and MRR (mean reciprocal rank).…”

Section: Previous Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Locating bugs without looking back

Dilshener

Wermelinger

2017

Autom Softw Eng

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Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We compare our approach to eight others, using their own five metrics on their own six open source projects. Out of 30 performance indicators, we improve 27 and equal 2. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. These results show the applicability of our approach to software projects without history.

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Machine Learning for Solving Unstructured Problems

Zhang

2023

Translational Systems Sciences

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Bug Localization Based on Code Change Histories and Bug Reports

Cited by 45 publications

References 19 publications

Where2Change: Change Request Localization for App Reviews

Where2Change: Change Request Localization for App Reviews

Locating bugs without looking back

Machine Learning for Solving Unstructured Problems

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