Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability
Markus Schnappinger, Arnaud Fietzke, Alexander Pretschner
Abstract
Before controlling the quality of software systems, we need to assess it. In the case of maintainability, this often happens with manual expert reviews. Current automatic approaches have received criticism because their results often do not reflect the opinion of experts or are biased towards a small group of experts. We use the judgments of a significantly larger expert group to create a robust maintainability dataset. In a large scale survey, 70 professionals assessed code from 9 open and closed source Java projects with a combined size of 1.4 million source lines of code. The assessment covers an overall judgment as well as an assessment of several subdimensions of maintainability. Among these subdimensions, we present evidence that understandability is valued the most by the experts. Our analysis also reveals that disagreement between evaluators occurs frequently. Significant dissent was detected in 17% of the cases. To overcome these differences, we present a method to determine a consensus, i.e. the most probable true label. The resulting dataset contains the consensus of the experts for more than 500 Java classes. This corpus can be used to learn precise and practical classifiers for software maintainability.