Implicit Social Network Model for Predicting and Tracking the Location of Faults

Chen, Ing-Xiang; Yang, Chengtao; Lu, Ting-Kun; Jaygarl, Hojun

doi:10.1109/compsac.2008.162

Cited by 16 publications

(8 citation statements)

References 24 publications

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“…As in [11], [12], [38], the accuracy rates of all folds are averaged to show the prediction power of each scheme. Figure 8 (a) presents the performance distribution of the Subversion project in the top-n recommendations, 1 ≤ n ≤ 50.…”

Section: Resultsmentioning

confidence: 99%

“…In this study, at most 50 candidates were considered because for a project of over nearly one thousand source files, such as AspectJ and ArgoUML, a top-50 recommendation list covers a range to help developers focus on the top 5% of potential locations and reduce the search efforts over the overall FP modules. As in [11], [12], [38], the prediction accuracy was assessed by regarding a correct prediction in the recommendation list as a hit. Therefore, the accuracy is measured as the percentage of correct hits as follows:…”

Section: Methodsmentioning

confidence: 99%

“…Although these approaches have shown the effectiveness in bug localization, they require software source code to gather execution profile information or code distribution features. To the best of our knowledge, only the work in [12] proposes a PageRank-based social network model to predict FP modules without considering source code. However, this social network model cannot get consistent improvements over an SVM scheme in different software projects because there may be limited social network relationships between some bug reports and the fixed locations.…”

Section: Related Workmentioning

confidence: 99%

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Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules

Chen

Yang

2010

IEICE Trans. Inf. & Syst.

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SUMMARYAutomated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the colocation relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules

Chen

Yang

2010

IEICE Trans. Inf. & Syst.

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“…Chen et al [6] apply PageRank to construct explicit links to connect suspicious program entities. Their approach cannot handle those faults that are not obviously suspicious, while the present paper aims to find such kind of fault effectively.…”

Section: Threats To Validitymentioning

confidence: 99%

Precise Propagation of Fault-Failure Correlations in Program Flow Graphs

Zhang

Chan

Tse

et al. 2011

2011 IEEE 35th Annual Computer Software and Applications Conference

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Statistical fault localization techniques find suspicious faulty program entities in programs by comparing passed and failed executions. Existing studies show that such techniques can be promising in locating program faults. However, coincidental correctness and execution crashes may make program entities indistinguishable in the execution spectra under study, or cause inaccurate counting, thus severely affecting the precision of existing fault localization techniques. In this paper, we propose a BlockRank technique, which calculates, contrasts, and propagates the mean edge profiles between passed and failed executions to alleviate the impact of coincidental correctness. To address the issue of execution crashes, BlockRank identifies suspicious basic blocks by modeling how each basic block contributes to failures by apportioning their fault relevance to surrounding basic blocks in terms of the rate of successful transition observed from passed and failed executions. BlockRank is empirically shown to be more effective than nine representative techniques on four real-life mediumsized programs.

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“…impacted by the new CR must be retrieved. They used TF‐IDF , LSI , and LDA for document representation, and algorithms for reasoning, including the following: SVM , Naive Bayes , J48 , PageRank , and cosine similarity function .…”

Section: Challenges: Improving the Management Of Change Requestmentioning

confidence: 99%

Challenges and opportunities for software change request repositories: a systematic mapping study

Cavalcanti

Neto

Machado

et al. 2013

J Software Evolu Process

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Software maintenance starts as soon as the first artifacts are delivered and is essential for the success of the software. However, keeping maintenance activities and their related artifacts on track comes at a high cost. In this respect, change request (CR) repositories are fundamental in software maintenance. They facilitate the management of CRs and are also the central point to coordinate activities and communication among stakeholders. However, the benefits of CR repositories do not come without issues, and commonly occurring ones should be dealt with, such as the following: duplicate CRs, the large number of CRs to assign, or poorly described CRs. Such issues have led researchers to an increased interest in investigating CR repositories, by considering different aspects of software development and CR management. In this paper, we performed a systematic mapping study to characterize this research field. We analyzed 142 studies, which we classified in two ways. First, we classified the studies into different topics and grouped them into two dimensions: challenges and opportunities. Second, the challenge topics were classified in accordance with an existing taxonomy for information retrieval models. In addition, we investigated tools and services for CR management, to understand whether and how they addressed the topics identified.

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Implicit Social Network Model for Predicting and Tracking the Location of Faults

Cited by 16 publications

References 24 publications

Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules

Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules

Precise Propagation of Fault-Failure Correlations in Program Flow Graphs

Challenges and opportunities for software change request repositories: a systematic mapping study

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