Computing contextual metric thresholds

Foucault, Matthieu; Palyart, Marc; Falleri, Jean‐Rémy; Blanc, Xavier

doi:10.1145/2554850.2554997

Cited by 23 publications

(23 citation statements)

References 23 publications

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“…Each quasi-experiment used one solution to define the thresholds of software metrics presented in the literature. The solutions used are the ones presented by Shatnawi [9] and Foucault et al [6]. The defined thresholds are the dependent variable.…”

Section: Methodsmentioning

confidence: 99%

“…In the literature, few metrics have defined thresholds. Furthermore, many researchers have proposed different approaches to define them [3,4,6,14,15,9,16,8,10].…”

Section: Thresholds Definitionmentioning

confidence: 99%

“…In Foucault et al [6], a solution based on statistical methods was presented. This approach is based on (i) double sampling [18] to randomly selects projects samples; and (ii) bootstrap to estimate the thresholds based on quartiles.…”

Section: Thresholds Definitionmentioning

confidence: 99%

“…From percentile cuts, the categorization of thresholds was defined as good, moderate and bad. Foucault et al [6] and Alves et al [3] also used percentile cuts to compute thresholds. Foucault et al [6] used a statistical analysis called bootstrap [7] to estimate the confidence interval of the percentile.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the number of referrals, we used the approaches proposed by Foucault et al [6] and by Shatnawi [9]. As the factor of the study, we used a variable with two possible treatments: to consider the context or not.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An empirical study on the influence of context in computing thresholds for Chidamber and Kemerer metrics

Santos¹,

Saraiva

Perkusich

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Software metrics have a fundamental role in the process of software quality management. However, in most cases, they are only used to quantify attributes, not supporting decision-making during the software life cycle. To support decision-making, it is necessary to give them by defining thresholds. In the literature, several approaches have been proposed with this purpose. On the other hand, most of them do not consider context factors such as the domain. Given this, in this paper, we evaluate if context factors influence the definition of thresholds for software metrics. Our work is restricted to Chidamber and Kemerer metrics, due to availability of data. We conducted an empirical study composed of two quasi-experiments. Each quasi-experiment uses an approach presented in the literature to define thresholds for software metrics, with the defined thresholds as the dependent variable. As the factor, we used a variable with two possible treatments: to consider the context or not. To define context, we used factors presented in the literature. As the objects of study, we used the source code of fifteen Java-based open-source projects. For measurement purposes, we used the six original Chidamber and Kemerer metrics. For both quasi-experiments, the accuracy of the definition of thresholds improved by considering the context. Therefore, we concluded that context factors influence the definition of the threshold for Chidamber and Kemerer metrics, which is an indicator that it influences other software metrics.

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Section: Methodsmentioning

confidence: 99%

“…In the literature, few metrics have defined thresholds. Furthermore, many researchers have proposed different approaches to define them [3,4,6,14,15,9,16,8,10].…”

Section: Thresholds Definitionmentioning

confidence: 99%

Section: Thresholds Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An empirical study on the influence of context in computing thresholds for Chidamber and Kemerer metrics

Santos¹,

Saraiva

Perkusich

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Deriving metrics thresholds using log transformation

Shatnawi

2015

J. Softw. Evol. and Proc.

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Software metrics are surrogates of many software quality factors such as fault proneness, reusability, and maintenance effort. Software metrics are numbers collected from software code to assess and evaluate where problems are more probable to happen. These numbers are used to flag warnings of the problematic parts of software code using threshold values. However, the proposed techniques did not consider the data distribution and skewness in data. In this research, we aim to propose a methodology based on log transformation to improve the metrics quality. To explore the effect of log transformation on data analysis, we conduct analysis of using software metrics after transformation in identifying fault-prone areas on multireleases of 11 products (41 releases). The results show that the log transformation can be used to derive threshold values for all metrics under investigation. The results of the transformation are then used to conduct fault-proneness classification based on threshold values and compared against the results without transformation. The fault classification with transformation was more successful than without transformation.

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Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks

Perkusich

Gorgônio

Almeida

et al. 2016

J Software Evolu Process

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Scrum is a simple process to understand, but hard to adopt. Therefore, there is a need for resources to assist on its adoption. In this paper, we present the process followed to build a Bayesian network to assist on the assessment of the quality of the software process in the context of Scrum projects. The model provides data to help Scrum Masters lead the improvement of business value delivery of Scrum teams. The process is divided into 2 phases. In the first phase, we built the Bayesian network based on expert knowledge extracted from the literature and experts. We used a top‐down approach and reasoning to define the key metrics necessary to build the models and their relationships. In the second phase, we updated the Bayesian network based on limitations of the first version. We validated the Bayesian network inferences with 10 simulated scenarios. Comparing both versions, for all scenarios, we improved the accuracy of the inferences. Therefore, we concluded that the Bayesian networks adequately represent Scrum projects from the viewpoint of the Scrum Master. Finally, the model built is in conformance with agile methods tailoring and can be adapted to any Scrum team.

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Computing contextual metric thresholds

Cited by 23 publications

References 23 publications

An empirical study on the influence of context in computing thresholds for Chidamber and Kemerer metrics

An empirical study on the influence of context in computing thresholds for Chidamber and Kemerer metrics

Deriving metrics thresholds using log transformation

Assisting the continuous improvement of Scrum projects using metrics and Bayesian networks

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