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
A Software Defect Prediction Method Based on Program Semantic Feature Mining
TL;DR: Wang et al. as mentioned in this paper proposed a software defect prediction method based on the program semantics feature mining (PSFM) method, which extracted the semantic information from the code grammatical structure information and code text information.
Journal ArticleDOI
Causally Remove Negative Confound Effects of Size Metric for Software Defect Prediction
TL;DR: The prediction model’s performance can, in general, be improved after removing the negative confounding effects of size metric, and this paper proposes a method that can causally remove these effects.
Book ChapterDOI
Early Reliability Prediction Model Integrating Halstead’s Metrics and Fuzzy Usage
TL;DR: A novel fuzzy model is proposed to predict the reliability of the component-based system and was validated statistically by comparing the predicted and estimated value of reliability.
Posted Content
Predicting Relative Thresholds for Object Oriented Metrics
TL;DR: In this article, the relationship between system size (as a contextual factor) and metric thresholds is investigated, and the objective is to build predictive models that estimate thresholds based solely on system size, and assess the feasibility of this approach as a threshold estimation method.
References
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Journal ArticleDOI
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.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
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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.