Predicting Defects for Eclipse

Zimmermann, Thomas; Premraj, Rahul; Zeller, Andreas

doi:10.1109/promise.2007.10

Cited by 594 publications

(557 citation statements)

References 14 publications

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“…Explicit variables specification to predict defect intensity of a release Zimmermann et al [28] Goal is implicitly described Implicit research question with no hypotheses Implicit variables specifications to predict module defect proneness Koru et al [8] Implicit goal description Implicit research question with no hypotheses Implicit variables specifications to predict module defect proneness Nagappan et al [16] Implicit goal description…”

Section: Extraction Of Findings and Discussionmentioning

confidence: 99%

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A Framework for Defect Prediction in Specific Software Project Contexts

Wahyudin

Ramler

Biffl

2011

Software Engineering Techniques

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Abstract. Software defect prediction has drawn the attention of many researchers in empirical software engineering and software maintenance due to its importance in providing quality estimates and to identify the needs for improvement from project management perspective. However, most defect prediction studies seem valid primarily in a particular context and little concern is given on how to find out which prediction model is well suited for a given project context. In this paper we present a framework for conducting software defect prediction as aid for the project manager in the context of a particular project or organization. The framework has been aligned with practitioners' requirements and is supported by our findings from a systematical literature review on software defect prediction. We provide a guide to the body of existing studies on defect prediction by mapping the results of the systematic literature review to the framework.

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Section: Extraction Of Findings and Discussionmentioning

confidence: 99%

“…Proposed framework was used for commercial context [11] Zimmermann et al [28] Cross validation of trained prediction model in different releases and levels of observation N/A Koru et al [8] Cross validation of trained prediction model with different class of data…”

Section: Explicit Research Hypothesesmentioning

confidence: 99%

A Framework for Defect Prediction in Specific Software Project Contexts

Wahyudin

Ramler

Biffl

2011

Software Engineering Techniques

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“…The identification of faulty source code entities is generally based on product metrics, such as size, cohesion, or coupling (e.g., [1], [2], [3]), as well as process-oriented metrics, such as number of file changes (e.g., [4]). However, we believe that fault proneness is a complex phenomenon hardly captured solely by structural characteristics of code entities.…”

Section: Introductionmentioning

confidence: 99%

Physical and conceptual identifier dispersion: Measures and relation to fault proneness

Arnaoudova

Eshkevari

Oliveto

et al. 2010

2010 IEEE International Conference on Software Maintenance

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Abstract-Poorly-chosen identifiers have been reported in the literature as misleading and increasing the program comprehension effort. Identifiers are composed of terms, which can be dictionary words, acronyms, contractions, or simple strings. We conjecture that the use of identical terms in different contexts may increase the risk of faults. We investigate our conjecture using a measure combining term entropy and term context coverage to study whether certain terms increase the odds ratios of methods to be fault-prone. Entropy measures the physical dispersion of terms in a program: the higher the entropy, the more scattered across the program the terms. Context coverage measures the conceptual dispersion of terms: the higher their context coverage, the more unrelated the methods using them. We compute term entropy and context coverage of terms extracted from identifiers in Rhino 1.4R3 and ArgoUML 0.16. We show statistically that methods containing terms with high entropy and context coverage are more fault-prone than others.

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“…In this context, we refer to defects that have been fixed by means of a change committed in a versioning system-Concurrent Versions Systems (CVS) 1 or SubVersioN (SVN) 2 for exampleand which commit note refers to a bug report posted on a bug-tracking system [31], Bugzilla in our case.…”

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confidence: 99%