Repository Metrics Research Articles

A longitudinal study on the temporal validity of software samples

ContextIn Empirical Software Engineering, it is crucial to work with representative samples that reflect the current state of the software industry. An important consideration, especially in rapidly changing fields like software development, is that if we use a sample collected years ago, it should continue to represent the same population in the present day to produce generalizable results. However, it is seldom the case in which a software sample built several years ago accurately depicts the current state of the development industry. Nevertheless, many recent studies rely on rather old datasets (seven or more years of age) to conduct their investigations. ObjectiveTo analyze the evolution of a population of open-source projects, determine the likelihood of detecting significant differences over time, and study the activity history of the projects. MethodWe performed a longitudinal study with 72 snapshots of quality projects from Github, covering the period between July 1st 2017 and June 1st 2023. We recorded monthly values of seven repository metrics (contributors, commits, closed pull-requests, merged pull-requests, closed issues, number of stars and forks), encompassing data from a total of 1991 repositories. ResultsWe observed significant changes in all the metrics evaluated, with most cases showing negligible to small effect sizes. Notably, merged pull-requests registered medium effect sizes. The evolution was not equal in all the metrics, however, after five years it was unlikely that a sample of projects remained representative for any of the analyzed metrics, showing probabilities below 25%. ConclusionAlthough the temporal validity of a sample depends on the specific data being studied, employing datasets created several years ago does not appear to be a sound strategy if the aim is to produce results that can be extrapolated to the current state of the population.

Improved software fault prediction using new code metrics and machine learning algorithms

Many code metrics exist for bug prediction. However, these metrics are based on the trivial count of code properties and are not sufficient. This research article proposes three new code metrics based on class complexity, coupling, and cohesion to fill the gap. The Promise repository metrics suite's complexity, coupling, and cohesion metrics are replaced by the proposed metrics, and a new metric suite is generated. Experiments show that the proposed metrics suite gives more than 2 % improvement in AUC and precision and approximately 1.5 % in f1-score and recall with fewer code metrics than the existing metrics suite.

Qualitative Clustering of Software Repositories Based on Software Metrics

Software repositories contain a wealth of information about the aspects related to software development process. For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering. Software repository clustering has been applied in studying software ecosystems such as GitHub, defect and technical debt prediction, software remodularization. Although some interesting insights have been reported, the considered studies exhibited some limitations. The limitations are associated with the use of individual clustering methods and manifesting in the shortcomings of the obtained results. In this study, to alleviate the existing limitations we engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clustering. To our knowledge, this study is the first to apply the consensus clustering approach to analyze software repositories and one of the few to apply the consensus clustering to software metrics. Intensive experimental studies are reported for software repository metrics data consisting of a number of software repositories each described by software metrics.We revealed seven clusters of software repositories and relate them to developers’ activity. It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner’s hype cycle.