The identification, categorization, and evaluation of model-based behavioral decay in design patterns

Abstract

Software quality assurance (QA) techniques seek to provide software developers and managers with the methods and tools necessary to monitor their software product to encourage fast, on-time, and bug-free releases for their clients. Ideally, QA methods and tools provide significant value and highly-specialized results to product stakeholders, while being fully incorporated into an organization's process and with actionable and easy-to-interpret outcomes. However, modern QA techniques fall short on these goals because they only feature structural analysis techniques, which do not fully illuminate all intricacies of a software product. Additionally, many modern QA methods are not capable of capturing domain-specific concerns, which suggests their results are not fulfilling their potential. To assist in the remediation of these issues, we have performed a comprehensive study to explore an unexplored phenomenon in the field of QA, namely model-based behavioral analysis. In this sense, behavioral analysis refers to the mechanisms that occur in a software product as the product is executing its code, at system run-time. We approach this problem from a model-based perspective because models are not tied to program-specific behaviors, so findings are more generalizable. Our procedure follows an intuitive process, involving first the identification of model-based behavioral issues, then the classification and categorization of these behavioral issues into a taxonomy, and finally the evaluation of them in terms of their effect on software quality. Our results include a taxonomy that captures and provides classifications for known model-based behavioral issues. We identified relationships between behavioral issues and existing structural issues to illustrate that the inclusion of behavioral analysis provides a new perspective into the inner mechanisms of software systems. We extended an existing state-of-the-art operational software quality measurement technique to incorporate these newfound behavioral issues. Finally, we used this quality extension to evaluate the effects of behavioral issues on system quality, and found that software quality has a strong inverse relationship with behavioral issues.