Mining configuration constraints: Static analyses and empirical results

Sarah Nadi, Thorsten Berger, Christian Kästner, Krzysztof Czarnecki

Research output: Contribution to journalConference articlepeer-review


Highly-configurable systems allow users to tailor the software to their specific needs. Not all combinations of configuration options are valid though, and constraints arise for technical or non-technical reasons. Explicitly describing these constraints in a variability model allows reasoning about the supported configurations. To automate creating variability models, we need to identify the origin of such configuration constraints. We propose an approach which uses build-time errors and a novel feature-effect heuristic to automatically extract configuration constraints from C code. We conduct an empirical study on four highly-configurable open-source systems with existing variability models having three objectives in mind: evaluate the accuracy of our approach, determine the recoverability of existing variability-model constraints using our analysis, and classify the sources of variability-model constraints. We find that both our extraction heuristics are highly accurate (93% and 77% respectively), and that we can recover 19% of the existing variability-models using our approach. However, we find that many of the remaining constraints require expert knowledge or more expensive analyses. We argue that our approach, tooling, and experimental results support researchers and practitioners working on variability model re-engineering, evolution, and consistency-checking techniques.

Original languageEnglish (US)
Pages (from-to)140-151
Number of pages12
JournalProceedings - International Conference on Software Engineering
Issue number1
StatePublished - May 31 2014
Event36th International Conference on Software Engineering, ICSE 2014 - Hyderabad, India
Duration: May 31 2014Jun 7 2014


  • empirical software engineering
  • feature models
  • reverse engineering
  • software product lines
  • static analysis
  • Variability models

ASJC Scopus subject areas

  • Software

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