Techniques for methodically exploring software development alternatives

2018-07-25Designing and maintaining a software system’s architecture typically involve making numerous design decisions, each potentially affecting the system’s functional and nonfunctional properties. Understanding these design decisions can help inform future decisions and implementation choices, and can avoid introducing architectural inefficiencies later. Unfortunately, the support for engineers to make these decisions is generally lacking. There is a relative shortage of techniques, tools, and empirical studies pertaining to architectural design decisions. Moreover, design decisions are rarely well documented and are typically a lost artifact of the architecture creation and maintenance process. The loss of this information can thus hurt development. To address these shortcomings, we develop a set of techniques to enable methodical exploration of such decisions and their effects. ❧ We develop a technique, named RecovAr , for automatically recovering design decisions from the project’s readily available history artifacts, such as an issue tracker and version control repository. RecovAr uses state-of-the-art architectural recovery techniques on a series of version control commits and maps those commits to issues to identify decisions that affect system architecture. While some decisions can still be lost through this process, our evaluation on two large open-source systems with over 8 years of development each shows that RecovAr has the recall of 75% and a precision of 77%. To create RecovAr, we formally define architectural design decisions and develop an approach for tracing such decisions in project histories. Additionally, the work introduces methods to classify whether decisions are architectural and to map decisions to code elements. ❧ Building on RecovAr , we create PredictAr . PredictAr aims to prevent the consequences of inadvertent architectural change. The result of such changes is accumulation of technical debt and deterioration of software quality. In this dissertation 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. We develop a predictive model that is able to identify the architectural significance 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. ❧ We close the loop by helping engineers to not only predict and recover architectural design decisions, but also make new design decisions that are informed and well-considered. Recent studies have shown that the number of available design alternatives grows rapidly with system size, creating an enormous space of intertwined design concerns. This dissertation presents eQual, a novel model-driven technique for simulation-based assessment of architectural designs that helps architects understand and explore the effects of their decisions. We demonstrate that eQual effectively explores massive spaces of design alternatives and significantly outperforms state-of-the-art approaches, without being cumbersome for architects to us