Defect Data Analysis Based on Extended Association Rule Mining

Morisaki, Shuji; Monden, Akito; Matsumura, Tomoko; Tamada, Haruaki; Matsumoto, Kenichi

doi:10.1109/msr.2007.5

Cited by 27 publications

(17 citation statements)

References 10 publications

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“…Adrian Bachmann, et al, [9] explored the process data quality and characteristics which have an influence on the bug fixing process and the process quality as measured by the process data influence on the product quality. Shuji Morisaki, et al, [10] described the study to reveal rules associated with defect correction effort and describes the extracted rules like a higher severity defect requires greater correction effort; defects detected in coding unit testing, were easily corrected. Syed Nadeem Ahsan, et al, [11] …”

Section: Literature Reviewmentioning

confidence: 99%

How Much Effort Needed to Fix the Bug? A Data Mining Approach for Effort Estimation and Analysing of Bug Report Attributes in Firefox

Vijayakumar

Bhuvaneswari

2014

2014 International Conference on Intelligent Computing Applications

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Estimating the effort required to fix a bug is a significant task for the project manager to determine the project release. Among various ways to estimate the effort, analysis of bug report attributes proved excellent results. In this paper the effort required to fix the bug on the components of Firefox application is studied. A framework has been charted for analysing the feature attributes which on imparting association rule mining process resulted with dictating rules which provide the effort succumb to fix the bugs of a particular component. The bug reports used for this study are extracted from Bugzilla, an open source bug repository. These bug reports provides a variety of categorical data from previous projects. Analysis of this can improve the planning of personnel to fix the bug and raise the quality of bug reports.

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Section: Literature Reviewmentioning

confidence: 99%

How Much Effort Needed to Fix the Bug? A Data Mining Approach for Effort Estimation and Analysing of Bug Report Attributes in Firefox

Vijayakumar

Bhuvaneswari

2014

2014 International Conference on Intelligent Computing Applications

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“…If this is not possible, then at least somebody outside the research group should perform the manual verification [14]. It is also suggested that rigorous inspection and testing is applied to the source code and scripts that implement the analysis approach since wrong output might not necessarily be due to poor heuristics, but simply due to defective implementations [44]. Finally, researchers suggest normalization in many scenarios.…”

Section: Comments From 5 Papers]mentioning

confidence: 99%

The MSR Cookbook: Mining a decade of research

Hemmati

Nadi

Baysal

et al. 2013

2013 10th Working Conference on Mining Software Repositories (MSR)

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Abstract-The Mining Software Repositories (MSR) research community has grown significantly since the first MSR workshop was held in 2004. As the community continues to broaden its scope and deepens its expertise, it is worthwhile to reflect on the best practices that our community has developed over the past decade of research. We identify these best practices by surveying past MSR conferences and workshops. To that end, we review all 117 full papers published in the MSR proceedings between 2004 and 2012. We extract 268 comments from these papers, and categorize them using a grounded theory methodology. From this evaluation, four high-level themes were identified: data acquisition and preparation, synthesis, analysis, and sharing/replication. Within each theme we identify several common recommendations, and also examine how these recommendations have evolved over the past decade. In an effort to make this survey a living artifact, we also provide a public forum that contains the extracted recommendations in the hopes that the MSR community can engage in a continuing discussion on our evolving best practices.

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“…Morisaki et al [14] proposed an extended association rule mining method that takes advantage of interval and ratio scale variables, instead of simply replacing them into nominal or ordinal variables. In the proposed method, an extended rule describes the statistical characteristic of quantitative variables (e.g.…”

Section: Related Work: Rule Mining In Software Engineeringmentioning

confidence: 99%

“…We used NEEDLE [14] as an association rule miner. To obtain an initial rule set (for applying rule reduction methods,) rules were mined from Mylyn v.1.0 dataset with threshold values: minimum support  support = .01 and minimum confidence  confidence = .75.…”

Section: Initial Rule Setmentioning

confidence: 99%

A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

Monden

Keung

Morisaki

et al. 2012

2012 19th Asia-Pacific Software Engineering Conference

Self Cite

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Abstract-Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining. Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible. Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included. Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only .015 (from .757 down to .742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from .426 to .441.) Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

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Defect Data Analysis Based on Extended Association Rule Mining

Cited by 27 publications

References 10 publications

How Much Effort Needed to Fix the Bug? A Data Mining Approach for Effort Estimation and Analysing of Bug Report Attributes in Firefox

How Much Effort Needed to Fix the Bug? A Data Mining Approach for Effort Estimation and Analysing of Bug Report Attributes in Firefox

The MSR Cookbook: Mining a decade of research

A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

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