Toward Predicting Architectural Significance of Implementation Issues

Arman Shahbazian, Daye Nam, Nenad Medvidovic

Summary

predictar_overview

In a software system’s development lifecycle, engineers make numerous design decisions that subsequently cause architectural change in the system. Previous studies have shown that, more often than not, these architectural changes are unintentional by-products of continual software maintenance tasks. The result of inadvertent architectural changes is accumulation of technical debt and deterioration of software quality. Despite their important implications, there is a relative shortage of techniques, tools, and empirical studies pertaining to architectural design decisions. In this paper, we take a step toward addressing that scarcity by using the information in the issue and code repositories of open-source software systems to investigate the cause and frequency of such architectural design decisions. Furthermore, building on these results, we develop a predictive model that is able to identify the architectural signi cance of newly submitted issues, thereby helping engineers to prevent the adverse effects of architectural decay. The results of this study are based on the analysis of 21,062 issues affecting 301 versions of 5 large open-source systems for which the code changes and issues were publicly accessible.

Publications

Arman Shahbazian, Daye Nam, and Nenad Medvidovic. (2018) Toward Predicting Architectural Significance of Implementation Issues. 15th Working Conference on Mining Software Repositories (MSR).

Data

Key system ARC ACDC Type Priority Title AffectedVersions FixVersions