Studying re-opened bugs in open source software

Shihab, Emad; Ihara, Akinori; Kamei, Yasutaka; Ibrahim, Walid; Ohira, Masaichi; Adams, Bram; Hassan, Ahmed E.; Matsumoto, Ken

doi:10.1007/s10664-012-9228-6

Cited by 114 publications

(121 citation statements)

References 44 publications

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“…Our F-measure is 69.23%. Comparing with many other studies performing other prediction tasks in software engineering research literature, e.g., [31], [32], our F-measure is comparable or higher. Relative Improvement…”

Section: ) Rq1: Overall Accuracysupporting

confidence: 54%

Will Fault Localization Work for These Failures? An Automated Approach to Predict Effectiveness of Fault Localization Tools

2013

2013 IEEE International Conference on Software Maintenance

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Abstract-Debugging is a crucial yet expensive activity to improve the reliability of software systems. To reduce debugging cost, various fault localization tools have been proposed. A spectrum-based fault localization tool often outputs an ordered list of program elements sorted based on their likelihood to be the root cause of a set of failures (i.e., their suspiciousness scores). Despite the many studies on fault localization, unfortunately, however, for many bugs, the root causes are often low in the ordered list. This potentially causes developers to distrust fault localization tools. Recently, Parnin and Orso highlight in their user study that many debuggers do not find fault localization useful if they do not find the root cause early in the list.To alleviate the above issue, we build an oracle that could predict whether the output of a fault localization tool can be trusted or not. If the output is not likely to be trusted, developers do not need to spend time going through the list of most suspicious program elements one by one. Rather, other conventional means of debugging could be performed. To construct the oracle, we extract the values of a number of features that are potentially related to the effectiveness of fault localization. Building upon advances in machine learning, we process these feature values to learn a discriminative model that is able to predict the effectiveness of a fault localization tool output. In this preliminary work, we consider an output of a fault localization tool to be effective if the root cause appears in the top 10 most suspicious program elements. We have experimented our proposed oracle on 200 faulty programs from Space, NanoXML, XML-Security, and the 7 programs in Siemens test suite. Our experiments demonstrate that we could predict the effectiveness of fault localization tool with a precision, recall, and F-measure (harmonic mean of precision and recall) of 54.36%, 95.29%, and 69.23%. The numbers indicate that many ineffective fault localization instances are identified correctly, while only very few effective ones are identified wrongly.

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Section: ) Rq1: Overall Accuracysupporting

confidence: 54%

Will Fault Localization Work for These Failures? An Automated Approach to Predict Effectiveness of Fault Localization Tools

2013

2013 IEEE International Conference on Software Maintenance

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“…[13][14][15]) has shown that task repetitions (i.e. repetitions in the life-cycle of an issue) are considered as a degrading factor of the overall quality of software development projects.…”

Section: B Potential Risk Factors For Software Issuesmentioning

confidence: 99%

“…[16]), re-opened bug prediction (e.g. [13,15]), severity/priority prediction (e.g. [54,55]), delays in the integration of a resolved issue to a release (e.g.…”

Section: Related Workmentioning

confidence: 99%

Characterization and Prediction of Issue-Related Risks in Software Projects

Choetkiertikul

Dam

Tran

et al. 2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories

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Abstract-Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing "risky" software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macroaveraged Mean Cost-Error and and 0.7-1.2 for Macro-averaged Mean Absolute Error.

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“…These techniques include bug triaging and developer recommendation [3][4][5][6], bug severity/priority assignment [7][8][9], duplicate bug report detection [10,11], bug fixing time prediction [12][13][14], and reopened bug prediction [15,16]. In general, the above techniques extract data from bug reports in bug tracking systems to build their prediction models.…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate ELBlocker, we use two metrics: F1-score [17,7,15,9] and cost effectiveness [24][25][26][27]. F1-score is a summary measure that combines both precision and recall.…”

Section: Introductionmentioning

confidence: 99%