Proceedings Article10.1145/2970276.2970359
Locus: locating bugs from software changes
Ming Wen,Rongxin Wu,Shing-Chi Cheung +2 more
- 25 Aug 2016
- pp 262-273
210
TL;DR: An IR-based approach Locus is proposed to locate bugs using software changes, which offer finer granularity than files and provide important contextual clues for bug-fixing, and it is shown that Locus outperforms existing techniques at the source file level localization significantly.
read more
Abstract: Various information retrieval (IR) based techniques have been proposed recently to locate bugs automatically at the file level. However, their usefulness is often compromised by the coarse granularity of files and the lack of contextual information. To address this, we propose to locate bugs using software changes, which offer finer granularity than files and provide important contextual clues for bug-fixing. We observe that bug inducing changes can facilitate the bug fixing process. For example, it helps triage the bug fixing task to the developers who committed the bug inducing changes or enables developers to fix bugs by reverting these changes. Our study further identifies that change logs and the naturally small granularity of changes can help boost the performance of IR-based bug localization. Motivated by these observations, we propose an IR-based approach Locus to locate bugs from software changes, and evaluate it on six large open source projects. The results show that Locus outperforms existing techniques at the source file level localization significantly. MAP and MRR in particular have been improved, on average, by 20.1% and 20.5%, respectively. Locus is also capable of locating the inducing changes within top 5 for 41.0% of the bugs. The results show that Locus can significantly reduce the number of lines needing to be scanned to locate the bug compared with existing techniques.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Context-aware patch generation for better automated program repair
Ming Wen,Junjie Chen,Rongxin Wu,Dan Hao,Shing-Chi Cheung +4 more
- 27 May 2018
TL;DR: This paper proposes CapGen, a context-aware patch generation technique that achieves a high precision of 84.00% and can prioritize the correct patches before 98.78% of the incorrect plausible ones, and studies the use of AST nodes' context information to estimate the likelihood.
An Empirical Study of Fault Localization Families and Their Combinations
TL;DR: Results reveal that a combined technique significantly outperforms any individual technique, suggesting that combination may be a desirable way to apply fault localization techniques and that future techniques should also be evaluated in the combined setting.
252
API method recommendation without worrying about the task-API knowledge gap
Qiao Huang,Xin Xia,Zhenchang Xing,David Lo,Xinyu Wang +4 more
- 03 Sep 2018
TL;DR: An API recommendation approach named BIKER (Bi-Information source based KnowledgE Recommendation) to tackle the lexical gap and knowledge gap between the natural language description of the programming task and the API description in API documentation is proposed.
227
FixMiner: Mining relevant fix patterns for automated program repair
Anil Koyuncu,Kui Liu,Tegawendé F. Bissyandé,Dongsun Kim,Jacques Klein,Martin Monperrus,Yves Le Traon +6 more
TL;DR: In this paper, the authors propose a systematic and automated approach to mine relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches, which can be leveraged to extract generic fix actions.
Improving IR-based bug localization with context-aware query reformulation
Mohammad Masudur Rahman,Chanchal K. Roy +1 more
- 26 Oct 2018
TL;DR: Zhang et al. as mentioned in this paper proposed a technique called BLIZZARD, which automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval, which determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization.
91
References
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
Henry B. Mann,D. R. Whitney +1 more
TL;DR: In this paper, the authors show that the limit distribution is normal if n, n$ go to infinity in any arbitrary manner, where n = m = 8 and n = n = 8.
•Book
Foundations of Statistical Natural Language Processing
Christopher D. Manning,Hinrich Schütze +1 more
- 28 May 1999
TL;DR: This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear and provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
Foundations of Statistical Natural Language Processing
TL;DR: This book is already in probability information theory and linguistic found it should be well grounded and indeed it is, this foundational text in human language applications who want to create the way.
3K
Simplifying and isolating failure-inducing input
Andreas Zeller,R. Hildebrandt +1 more
TL;DR: The delta debugging algorithm generalizes and simplifies the failing test case to a minimal test case that still produces the failure, and isolates the difference between a passing and a failingTest case.
When do changes induce fixes
Jacek Śliwerski,Thomas Zimmermann,Andreas Zeller +2 more
- 17 May 2005
TL;DR: In a first investigation of the MOZILLA and ECLIPSE history, it turns out that fix-inducing changes show distinct patterns with respect to their size and the day of week they were applied.