Threshold estimation from software metrics by using evolutionary techniques and its proposed algorithms, models

Padhy, Neelamadhab; Panigrahi, Rasmita; Neeraja, K.

doi:10.1007/s12065-019-00201-0

Cited by 21 publications

(5 citation statements)

References 28 publications

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“…For maximum GM (MGM) method, the criterion of the best candidate threshold is chosen to maximize the GM value. Because the GM indicator can keep the balance between the two accuracies and has been extensively used in previous studies, 14,[16][17][18][19] we applied the MGM method based on the GM indicator in this study to derive the thresholds for each metric as well.…”

Section: Metric Thresholds In Defect Predictionmentioning

confidence: 99%

Cross‐version defect prediction using threshold‐based active learning

Mei

Liu

et al. 2023

J Software Evolu Process

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Because defects in software modules (e.g., classes) might lead to product failure and financial loss, software defect prediction enables us to better understand and control software quality. Software development is a dynamic evolutionary process that may result in data distributions (e.g., defect characteristics) varying from version to version. In this case, effective cross‐version defect prediction (CVDP) is not easy to achieve. In this paper, we aim to investigate whether the defect prediction method of the threshold‐based active learning (TAL) can tackle the problem of the different data distribution between successive versions. Our TAL method includes two stages. At the active learning stage, a committee of investigated metrics is constructed to vote on the unlabeled modules of the current version. We pick up the unlabeled module with the median of voting scores to domain experts. The domain experts test and label the selected unlabeled module. Then, we merge the selected labeled module and the remaining modules with pseudo‐labels from the current version into the labeled modules of the prior version to form enhanced training data. Based on the training data, we derive the metric thresholds used for the next iteration. At the defect prediction stage, the iterations stop when a predefined threshold is reached. Finally, we use the cutoff threshold of voting scores, that is, 50%, to predict the defect‐prone of the remaining unlabeled modules. We evaluate the TAL method on 31 versions of 10 projects with three prevalent performance indicators. The results show that TAL outperforms the baseline methods, including three variations methods, two common supervised methods, and the state‐of‐the‐art method Hybrid Active Learning and Kernel PCA (HALKP). The results indicate that TAL can effectively address the different data distribution between successive versions. Furthermore, to keep the cost of extensive testing low in practice, selecting 5% of candidate modules from the current version is sufficient for TAL to achieve a good performance of defect prediction.

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Section: Metric Thresholds In Defect Predictionmentioning

confidence: 99%

Cross‐version defect prediction using threshold‐based active learning

Mei

Liu

et al. 2023

J Software Evolu Process

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“…These metrics capture different attributes, i.e., size, coupling, complexity, and refinement. In addition, papers [16,23,24,26,36,49,57,58,60,61,63,65,66] utilized CK metrics for analysis and measures and identified threshold values for these metrics. The McCabe complexity metrics were applied in papers [7,18,26,41,63].…”

Section: Q3 Which Metrics Combinations Have Been Used?mentioning

confidence: 99%

“…Catal et al [44] used additional metrics from the Halstead [44] approach. In the paper of Singh and Kahlon [41], coupling and cohesion metrics [61], and Li and Henry [21] metrics were applied. Lochmann [49] used static code checkers' rules as metrics.…”

Section: Q3 Which Metrics Combinations Have Been Used?mentioning

confidence: 99%

Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study

et al. 2021

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Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.

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“…Sin embargo, actualmente no se dispone de un paradigma de software que permita asegurar la calidad del producto de software móvil (Corral et al, 2015). Padhy et al (2019) señala que "las métricas de software desempeñan un papel importante en la industria del software," por esta razón varios autores han propuesto modelos y conjunto de métricas que han considerado relevantes para definir la calidad de una aplicación móvil, a partir de los atributos de la ISO/IEC 9126 tales como eficiencia, usabilidad, portabilidad entre otros.…”

Section: Introductionunclassified