Understanding cost drivers of software evolution: a quantitative and qualitative investigation of change effort in two evolving software systems

Abstract

Making changes to software systems can prove costly and it remains a challenge to understand the factors that affect the costs of software evolution. This study sought to identify such factors by investigating the effort expended by developers to perform 336 change tasks in two different software organizations. We quantitatively analyzed data from version control systems and change trackers to identify factors that correlated with change effort. In-depth interviews with the developers about a subset of the change tasks further refined the analysis. Two central quantitative results found that dispersion of changed code and volatility of the requirements for the change task correlated with change effort. The analysis of the qualitative interviews pointed to two important, underlying cost drivers: Difficulties in comprehending dispersed code and difficulties in anticipating side effects of changes. This study demonstrates a novel method for combining qualitative and quantitative analysis to assess cost drivers of software evolution. Given our findings, we propose improvements to practices and development tools to manage and reduce the costs.

Reducing the maintenance effort for parameterization of representative load tests using annotations

“…This comprises the cumbersome specifications of large JSON inputs and CSV files with multiple columns, as well as the introduced extensions that are candidates to be added to the core meta‐model. Second, we suggest conducting an empirical study for enriching our derived effort estimation models with precise measures of the effort in, for example, person days, related to Benestad et al . Furthermore, we detected a small but systematic error in the request rates generated by WESSBAS.…”

“…This is described in RQ3: To which degree can we reduce the maintenance effort for the evolution of manual parameterizations of generated load tests over changes in the target application's API? As described in Section , determining the concrete maintenance effort requires empirical studies related to , which is left for future work. Therefore, we utilize the metric θ for deriving formulas that can be compared asymptotically.…”

Review of methods for migrating software systems to microservices architecture

“…Due to the rapid change of business conditions and constant improvement of technologies, software systems constantly change to remain useful for their users (Benestad, Anda, & Arisholm, 2010), which indicates that software maintenance is very important for improvement of software quality (Z. Stojanov, J. Stojanov, & Dobrilović, 2019).…”

Modularization of Legacy Features by Relocation and Reconceptualization: How Much is Enough?

“…Such misalignment was also reported to be one of the primary factors contributing to change effort and error-proneness during software evolution [4][12] [5].…”