Features in product lines and highly configurable systems can interact in ways contrary to developers’ intent . Current methods to identify suchunanticipated feature interactions are costly and inadequate . The contribution of the paper is to use program analysis to extract feature-relevant learning models from the source code in order to detectunwanted feature interactions . This enables betterunderstanding of latent interactions and identifies software components that should be tested together because their features interact in someconfigurations . The approach infers the constraints using feature-related data-flow dependency information. Evaluation in experiments on three softwareproduct line benchmarks and a highly configable system shows that this approach is fast and effective. The contribution is to support developers byautomatically detecting feature combinations in a new product or version thatcan interact in unwanted or unrecognized ways. This approach is

Author(s) : Seyedehzahra Khoshmanesh, Tuba Yavuz, Robyn R. Lutz

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Keywords : feature - interactions - approach - interact - product -

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