Journal ArticleDOI
Software fault prediction metrics
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TLDR
Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics and seem to be better at predicting post-release faults than any static code metrics.Abstract:
ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties. ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer's (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics. ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.read more
Citations
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Journal ArticleDOI
Exploring the software repositories of embedded systems: An industrial experience
Jakub Polaczek,Janusz Sosnowski +1 more
TL;DR: A novel approach to software repository analysis based on the fine grained exploration of issue tracking and code control repositories is proposed, which shows that finegrained repository analysis gives a broader and more complete view of project development, which may lead to its improvement.
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Ensemble Techniques-Based Software Fault Prediction in an Open-Source Project
TL;DR: Results showed that ensemble models performed better compared to machine learning and hybrid search-based algorithms and Bagging ensemble was found to be more effective in the prediction of faults in comparison to soft and hard voting.
Book ChapterDOI
Is Open-Source Software Valuable for Software Defect Prediction of Proprietary Software and Vice Versa?
TL;DR: The study concluded that when SDP models are trained using data from OSS projects then they are able to predict software defects for proprietary software with greater accuracy in comparison to SDP model predicting defects for OSS Projects when trained using proprietary software data.
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A stitch in time saves nine: Early improving code-first Web Services discoverability
TL;DR: This paper studies a number of simple and well-known code service refactorings that early reduce anti-pattern occurrences in WSDL documents and quantifies the effects of the refactoring — which directly modify OO metric values and indirectly alter anti- pattern values.
References
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The measurement of observer agreement for categorical data
J. R. Landis,Gary G. Koch +1 more
TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
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A metrics suite for object oriented design
TL;DR: This research addresses the needs for software measures in object-orientation design through the development and implementation of a new suite of metrics for OO design, and suggests ways in which managers may use these metrics for process improvement.
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A complexity measure
TL;DR: In this paper, a graph-theoretic complexity measure for managing and controlling program complexity is presented. But the complexity is independent of physical size, and complexity depends only on the decision structure of a program.
Journal ArticleDOI
A Complexity Measure
TL;DR: Several properties of the graph-theoretic complexity are proved which show, for example, that complexity is independent of physical size and complexity depends only on the decision structure of a program.