Quantitative Software Change Prediction in Open Source Web Projects Using Time Series Forecasting

Software change prediction (SCP) is used for the prediction of changes earlier in the software development life cycle. It identifies the files that are change prone. Software maintenance costs can be reduced with the help of accurate prediction of change-prone files. Most of the literature of SCP deals with the identification of a class as change prone or not change prone. In the present work, the amount of change in a web project in terms of line of code added (loc_added), line of code deleted (loc_deleted), and lines of code (LOC) are predicted using time series forecasting method of machine learning. Data of web projects is obtained from GIT repository using Pydriller Python package extractor. The obtained result showed that support vector machine (SVM) is good for prediction of loc_added and loc_removed while the random forest is good for the prediction of LOC. Results advocate the use machine learning techniques for forecasting changes amount in web projects.

Human Competency Assessment for Software Configuration Management

Late product delivery is a common problem in software projects. Late delivery can be attributed to software change especially in the development and maintenance phases, leading to more work than originally planned, thus increasing project completion time. One approach for managing software change is Software Configuration Management (SCM). SCM was first introduced in the 1970s to ensure the timely delivery of software products. To date, SCM implementation is supported by recognized standards, best practice, and countless commercial and proprietary tools. However, after more than 50 years, the issue of late software product delivery still prevails, questioning the practice of SCM in software organizations. One aspect of SCM that has received little focus in mainstream research is the human aspects. This study aims to identify how the competency of SCM practitioners can be assessed through the identification of SCM competency criteria and the development of a competency assessment framework. The framework was validated through expert reviews and case studies involving practitioners from the public sector, industry, institutes of higher learning and international organizations. Results confirmed the reliability of the SCM competency criteria and the plausibility of the competency assessment framework.

Improving the Accuracy of Static Source Code Based Software Change Impact Analysis Through Hybrid Techniques: A Review

Change is an inevitable phenomenon of life. This inevitability of change in the real world has made a software change an indispensable characteristic of software systems and a fundamental task of software maintenance and evolution. The continuous evolution process of software systems can greatly affect the systems’ quality and reliability if proper mechanisms to manage them are not adequately provided. Therefore, there is a need for automated techniques to effectively make an assessment of proposed software changes that may arise due to bug fixes, technological advancements, changing user requirements etc., before their implementation. Software Change Impact Analysis (CIA) is an essential activity for comprehending and identifying potential change impacts of software changes that can help prevent the system from entering into an erroneous state. Despite the emergence of different CIA techniques, they are yet to reach an optimal level of accuracy desired by software engineers. Consequently, researchers in recent years have come up with hybrid CIA techniques which are a blend of multiple CIA approaches, as a way of improving the accuracy of change impacts analysis techniques. This study presents these hybrid CIA techniques and how they improve accuracy. They are also compared and areas for further research are identified.