Logistic Regression Modeling of Software Quality

Khoshgoftaar, Taghi M.; Allen, Edward B.

doi:10.1142/s0218539399000292

Cited by 100 publications

(36 citation statements)

References 15 publications

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“…Besides providing an unbiased estimate of a classifier's generalization performance, the split-sample setup offers the advantage of enabling easy replication, which constitutes an important part of empirical research [2], [19], [49], [50]. Furthermore, its choice is motivated by the fact that the split-sample setup is the prevailing approach to assess predictive accuracy in software defect prediction [15], [16], [23], [28], [32], [33], [34], [37].…”

Section: Experimental Designmentioning

confidence: 99%

“…Various types of classifiers have been applied to this task, including statistical procedures [4], [28], [47], tree-based methods [24], [30], [43], [53], [58], neural networks [29], [31], and analogy-based approaches [15], [23], [32]. However, as noted in [48], [49], [59], results regarding the superiority of one method over another or the usefulness of metric-based classification in general are not always consistent across different studies.…”

Section: Introductionmentioning

confidence: 99%

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Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

Lessmann

Baesens

Mues

et al. 2008

IIEEE Trans. Software Eng.

1,019

663

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Abstract-Software defect prediction strives to improve software quality and testing efficiency by constructing predictive classification models from code attributes to enable a timely identification of fault-prone modules. Several classification models have been evaluated for this task. However, due to inconsistent findings regarding the superiority of one classifier over another and the usefulness of metric-based classification in general, more research is needed to improve convergence across studies and further advance confidence in experimental results. We consider three potential sources for bias: comparing classifiers over one or a small number of proprietary data sets, relying on accuracy indicators that are conceptually inappropriate for software defect prediction and cross-study comparisons, and, finally, limited use of statistical testing procedures to secure empirical findings. To remedy these problems, a framework for comparative software defect prediction experiments is proposed and applied in a large-scale empirical comparison of 22 classifiers over 10 public domain data sets from the NASA Metrics Data repository. Overall, an appealing degree of predictive accuracy is observed, which supports the view that metric-based classification is useful. However, our results indicate that the importance of the particular classification algorithm may be less than previously assumed since no significant performance differences could be detected among the top 17 classifiers.

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Section: Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

Lessmann

Baesens

Mues

et al. 2008

IIEEE Trans. Software Eng.

1,019

663

View full text Add to dashboard Cite

show abstract

“…Basili et al [4] analysed the applicability of Chidamber and Kemerer's Object Oriented set of metrics [10] with logistic regression to predict fault-prone code classes. Khoshgoftaar and Allen [27] also analysed logistic regression extended with prior probabilities and of misclassification costs.…”

Section: Related Workmentioning

confidence: 99%

Searching for rules to detect defective modules: A subgroup discovery approach

Rodríguez

Ruíz

Riquelme

et al. 2012

Information Sciences

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Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsistency, redundancy) not all classification algorithms are capable of dealing with this task conveniently. To deal with these problems, Subgroup Discovery (SD) algorithms can be used to find groups of statistically different data given a property of interest. We propose EDER-SD (Evolutionary Decision Rules for Subgroup Discovery), a SD algorithm based on evolutionary computation that induces rules describing only faultprone modules. The rules are a well-known model representation that can be easily understood and applied by project managers and quality engineers. Thus, rules can help them to develop software systems that can be justifiably trusted. Contrary to other approaches in SD, our algorithm has the advantage of working with continuous variables as the conditions of the rules are defined using intervals. We describe the rules obtained by applying our algorithm to seven publicly available datasets from the PROMISE repository * Corresponding author Email addresses: daniel.rodriguezg@uah.es (D. Rodríguez), robertoruiz@upo.es (R. Ruiz), riquelme@lsi.us.es (J.C. Riquelme), aguilar@upo.es (J.S. Aguilar-Ruiz) February 4, 2011 showing that they are capable of characterising subgroups of fault-prone modules. We also compare our results with three other well known SD algorithms and the EDER-SD algorithm performs well in most cases. Preprint submitted to Information Sciences

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“…In the following, we give a short introduction to logistic regression, full details can be found in [20] or [22].…”

Section: Logistic Regressionmentioning

confidence: 99%