A generic methodology for early identification of non-maintainable source code components through analysis of software releases

Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

“…To that end and given its significance as a quality attribute, several research efforts are directed towards proposing methodologies that aspire to assess the extent to which a source code component is maintainable. In this context, the majority of the proposed approaches construct models based on the values of static analysis metrics that quantify several aspects of the source code [8,18,19].…”

“…In an effort to overcome the aforementioned limitations and provide models that enable predicting maintainability, there are also approaches that employ release information as a way to monitor software evolution over time [27,28]. In a similar research direction, our prior work [8] suggested that the evolution of the values of static analysis metrics as reflected in their linear trends can be used as a maintainability indicator. In the context of this approach, we harness information residing in online code hosting facilities so as to identify non-maintainable code components and thus construct the ground truth upon which we build our maintainability evaluation models.…”

“…Maintainability is defined as the "degree of effectiveness and efficiency by which a product or system can be modified by the intended maintainers". The importance of producing maintainable software is also indicated by the fact that maintenance-related tasks (i.e., code refactoring given the existence of updated requirements or bugs) often require up to 80% of the total effort put into the software project [8]. To that end, several research efforts are directed towards the identification and construction of efficient methodologies along with the respective tools that enable evaluating maintainability [9].…”

“…Various metrics have been proposed to model maintainability that quantify several primary properties of the source code such as cohesion, complexity, coupling, and degree of inheritance [12,13]. These metrics are often used as the information basis upon constructing maintainability evaluation models and predictors [8,14]. Apart from harnessing this information for assessing the extent to which a source code component is maintainable, constructing models requires defining the appropriate thresholds, which is a non-trivial task often taken by experts who manually examine the source code so as to decide the desirable values.…”

“…In this work, we argue that assessing software maintainability is not a one-off procedure, but a constantly running process throughout the development lifecycle that requires being able to adapt to the constantly changing characteristics of the software project. Given the continuously increasing size and complexity of the software projects currently, which makes the manual examination of the source code unattainable, we extend the data-driven methodology proposed by Papamichail et al [8], which harnesses information residing in online code hosting facilities towards building a maintainability evaluation methodology based on the analysis of software releases. As opposed to the aforementioned approach, which involves analyzing various software projects without accounting for their score and given the highly context-dependent nature of maintainability, we focus on a specific domain and show how the scope of software projects indeed affects the software maintainability methodology.…”