Metrics for analyzing variability and its implementation in software product lines: A systematic literature review

摘要

Context:Software Product Line (SPL) development requires at least concepts for variability implementation and variability modeling for deriving products from a product line. These variability implementation concepts are not required for the development of single systems and, thus, are not considered in traditional software engineering. Metrics are well established in traditional software engineering, but existing metrics are typically not applicable to SPLs as they do not address variability management. Over time, various specialized product line metrics have been described in literature, but no systematic description of these metrics and their characteristics is currently available.Objective:This paper describes and analyzes variability-aware metrics, designed for the needs of software product lines. More precisely we restrict the scope of our study explicitly to metrics designed for variability models, code artifacts, and metrics taking both kinds of artifacts into account. Further, we categorize the purpose for which these metrics were developed. We also analyze to what extent these metrics were evaluated to provide a basis for researchers for selecting adequate metrics.Method:We conducted a systematic literature review to identify variability-aware implementation metrics. We discovered 42 relevant papers reporting metrics intended to measure aspects of variability models or code artifacts.Results:We identified 57 variability model metrics, 34 annotation-based code metrics, 46 code metrics specific to composition-based implementation techniques, and 10 metrics integrating information from variability model and code artifacts. For only 31 metrics, an evaluation was performed assessing their suitability to draw any qualitative conclusions.Conclusions:We observed several problematic issues regarding the definition and the use of the metrics. Researchers and practitioners benefit from the catalog of variability-aware metrics, which is the first of its kind. Also, the research community benefits from the identified observations in order to avoid those problems when defining new metrics.