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- Al Mamun, Md Abdullah, 1982, et al.
(author)
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Correlations of software code metrics : An empirical study
- 2017
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In: IWSM Mensura '17. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450348539 ; , s. 255-266
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Conference paper (peer-reviewed)abstract
- Background: The increasing up-trend of software size brings about challenges related to release planning and maintainability. Foreseeing the growth of software metrics can assist in taking proactive decisions regarding different areas where software metrics play vital roles. For example, source code metrics are used to automatically calculate technical debt related to code quality which may indicate how maintainable a software is. Thus, predicting such metrics can give us an indication of technical debt in the future releases of software. Objective: Estimation or prediction of software metrics can be performed more meaningfully if the relationships between different domains of metrics and relationships between the metrics and different domains are well understood. To understand such relationships, this empirical study has collected 25 metrics classified into four domains from 9572 software revisions of 20 open source projects from 8 well-known companies. Results: We found software size related metrics are most correlated among themselves and with metrics from other domains. Complexity and documentation related metrics are more correlated with size metrics than themselves. Metrics in the duplications domain are observed to be more correlated to themselves on a domain-level. However, a metric to domain level relationship exploration reveals that metrics with most strong correlations are in fact connected to size metrics. The Overall correlation ranking of duplication metrics are least among all domains and metrics. Contribution: Knowledge earned from this research will help to understand inherent relationships between metrics and domains. This knowledge together with metric-level relationships will allow building better predictive models for software code metrics. © 2017 Association for Computing Machinery.
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- Al Mamun, Md Abdullah, 1982, et al.
(author)
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Effects of measurements on correlations of software code metrics
- 2019
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In: Empirical Software Engineering. - : Springer Science and Business Media LLC. - 1382-3256 .- 1573-7616. ; 24:4, s. 2764-2818
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Journal article (peer-reviewed)abstract
- ContextSoftware metrics play a significant role in many areas in the life-cycle of software including forecasting defects and foretelling stories regarding maintenance, cost, etc. through predictive analysis. Many studies have found code metrics correlated to each other at such a high level that such correlated code metrics are considered redundant, which implies it is enough to keep track of a single metric from a list of highly correlated metrics.ObjectiveSoftware is developed incrementally over a period. Traditionally, code metrics are measured cumulatively as cumulative sum or running sum. When a code metric is measured based on the values from individual revisions or commits without consolidating values from past revisions, indicating the natural development of software, this study identifies such a type of measure as organic. Density and average are two other ways of measuring metrics. This empirical study focuses on whether measurement types influence correlations of code metrics.MethodTo investigate the objective, this empirical study has collected 24 code metrics classified into four categories, according to the measurement types of the metrics, from 11,874 software revisions (i.e., commits) of 21 open source projects from eight well-known organizations. Kendall's tau-B is used for computing correlations. To determine whether there is a significant difference between cumulative and organic metrics, Mann-Whitney U test, Wilcoxon signed rank test, and paired-samples sign test are performed.ResultsThe cumulative metrics are found to be highly correlated to each other with an average coefficient of 0.79. For corresponding organic metrics, it is 0.49. When individual correlation coefficients between these two measure types are compared, correlations between organic metrics are found to be significantly lower (with p <0.01) than cumulative metrics. Our results indicate that the cumulative nature of metrics makes them highly correlated, implying cumulative measurement is a major source of collinearity between cumulative metrics. Another interesting observation is that correlations between metrics from different categories are weak.ConclusionsResults of this study reveal that measurement types may have a significant impact on the correlations of code metrics and that transforming metrics into a different type can give us metrics with low collinearity. These findings provide us a simple understanding how feature transformation to a different measurement type can produce new non-collinear input features for predictive models.
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- Al Mamun, Md Abdullah, 1982, et al.
(author)
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Evolution of technical debt: An exploratory study
- 2019
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In: CEUR Workshop Proceedings. - : CEUR-WS. - 1613-0073. ; 2476, s. 87-102, s. 87-102
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Conference paper (peer-reviewed)abstract
- Context: Technical debt is known to impact maintainability of software. As source code files grow in size, maintainability becomes more challenging. Therefore, it is expected that the density of technical debt in larger files would be reduced for the sake of maintainability. Objective: This exploratory study investigates whether a newly introduced metric ‘technical debt density trend’ helps to better understand and explain the evolution of technical debt. The ‘technical debt density trend’ metric is the slope of the line of two successive ‘technical debt density’ measures corresponding to the ‘lines of code’ values of two consecutive revisions of a source code file. Method: This study has used 11,822 commits or revisions of 4,013 Java source files from 21 open source projects. For the technical debt measure, SonarQube tool is used with 138 code smells. Results: This study finds that ‘technical debt density trend’ metric has interesting characteristics that make it particularly attractive to understand the pattern of accrual and repayment of technical debt by breaking down a technical debt measure into multiple components, e.g., ‘technical debt density’ can be broken down into two components showing mean density corresponding to revisions that accrue technical debt and mean density corresponding to revisions that repay technical debt. The use of ‘technical debt density trend’ metric helps us understand the evolution of technical debt with greater insights.
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- Al Mamun, Md Abdullah, 1982, et al.
(author)
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Improving Code Smell Predictions in Continuous Integration by Differentiating Organic from Cumulative Measures
- 2019
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In: The Fifth International Conference on Advances and Trends in Software Engineering. - 2519-8394. - 9781510883741 ; , s. 62-71
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Conference paper (peer-reviewed)abstract
- Continuous integration and deployment are enablers of quick innovation cycles of software and systems through incremental releases of a product within short periods of time. If software qualities can be predicted for the next release, quality managers can plan ahead with resource allocation for concerning issues. Cumulative metrics are observed to have much higher correlation coefficients compared to non-cumulative metrics. Given the difference in correlation coefficients of cumulative and noncumulative metrics, this study investigates the difference between metrics of these two categories concerning the correctness of predicting code smell which is internal software quality. This study considers 12 metrics from each measurement category, and 35 code smells collected from 36,217 software revisions (commits) of 242 open source Java projects. We build 8,190 predictive models and evaluate them to determine how measurement categories of predictors and targets affect model accuracies predicting code smells. To further validate our approach, we compared our results with Principal Component Analysis (PCA), a statistical procedure for dimensionality reduction. Results of the study show that within the context of continuous integration, non-cumulative metrics as predictors build better predictive models with respect to model accuracy compared to cumulative metrics. When the results are compared with models built from extracted PCA components, we found better results using our approach.
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- Cochius-den Otter, S, et al.
(author)
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The CoDiNOS trial protocol: an international randomised controlled trial of intravenous sildenafil versus inhaled nitric oxide for the treatment of pulmonary hypertension in neonates with congenital diaphragmatic hernia
- 2019
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In: BMJ open. - : BMJ. - 2044-6055. ; 9:11, s. e032122-
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Journal article (peer-reviewed)abstract
- Congenital diaphragmatic hernia (CDH) is a developmental defect of the diaphragm that impairs normal lung development, causing pulmonary hypertension (PH). PH in CDH newborns is the main determinant for morbidity and mortality. Different therapies are still mainly based on ‘trial and error’. Inhaled nitric oxide (iNO) is often the drug of first choice. However, iNO does not seem to improve mortality. Intravenous sildenafil has reduced mortality in newborns with PH without CDH, but prospective data in CDH patients are lacking.Methods and analysisIn an open label, multicentre, international randomised controlled trial in Europe, Canada and Australia, 330 newborns with CDH and PH are recruited over a 4-year period (2018–2022). Patients are randomised for intravenous sildenafil or iNO. Sildenafil is given in a loading dose of 0.4 mg/kg in 3 hours; followed by continuous infusion of 1.6 mg/kg/day, iNO is dosed at 20 ppm. Primary outcome is absence of PH on day 14 without pulmonary vasodilator therapy and/or absence of death within the first 28 days of life. Secondary outcome measures include clinical and echocardiographic markers of PH in the first year of life. We hypothesise that sildenafil gives a 25% reduction in the primary outcome from 68% to 48% on day 14, for which a sample size of 330 patients is needed. An intention-to-treat analysis will be performed. A p-value (two-sided) <0.05 is considered significant in all analyses.Ethics and disseminationEthics approval has been granted by the ethics committee in Rotterdam (MEC-2017-324) and the central Committee on Research Involving Human Subjects (NL60229.078.17) in the Netherlands. The principles of the Declaration of Helsinki, the Medical Research Involving Human Subjects Act and the national rules and regulations on personal data protection will be used. Parental informed consent will be obtained.Trial registration numberNTR6982; Pre-results.
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