Detecting Feature Interactions in FORML Models

Beidu, Sandy; Atlee, Joanne M.

doi:10.1007/978-3-030-30985-5_14

Cited by 2 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study of feature interactions in the product-line literature by Soares, et al [61] found that 43% of the papers aimed to understand feature interaction at early stages of the software life cycle. Among this 43%, the majority used formal methods, especially feature-aware verification to automate detection of interactions [10,11], and model checking to measure behavioral changes when a new feature is added [12], as well as to detect conflicts among features [21,15]. However, such formal models are costly to create and typically not available for most real-world systems.…”

Section: Related Workmentioning

confidence: 99%

Learning Feature Interactions With and Without Specifications

Khoshmanesh,

Yavuz,

Lutz

2021

Preprint

View full text Add to dashboard Cite

Features in product lines and highly configurable systems can interact in ways that are contrary to developers' intent. Current methods to identify such unanticipated feature interactions are costly and inadequate. To address this problem we propose a new approach to learn feature interactions, both in those product lines where constraints on feature combinations are specified and in feature-rich configurable systems where such specifications often are not available. The contribution of the paper is to use program analysis to extract feature-relevant learning models from the source code in order to detect unwanted feature interactions. Where specifications of feature constraints are unavailable, our approach infers the constraints using feature-related data-flow dependency information. Evaluation in experiments on three software product line benchmarks and a highly configurable system shows that this approach is fast and effective. The contribution is to support developers by automatically detecting feature combinations in a new product or version that can interact in unwanted or unrecognized ways. This enables better understanding of latent interactions and identifies software components that should be tested together because their features interact in some configurations.

show abstract

Section: Related Workmentioning

confidence: 99%

Learning Feature Interactions With and Without Specifications

Khoshmanesh,

Yavuz,

Lutz

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A study of feature interactions in the product-line literature by found that 43% of the papers aimed to understand feature interaction at early stages of the software life cycle. Among this 43%, the majority used formal methods, especially featureaware verification to automate detection of interactions Apel et al ( , 2013c, and model checking to measure behavioral changes when a new feature is added , as well as to detect conflicts among features Calder and Miller (2006); Beidu and Atlee (2019). However, such formal models are costly to create and typically not available for most real-world systems.…”

Section: Related Workmentioning

confidence: 99%

Learning feature interactions with and without specifications

Khoshmanesh

View full text Add to dashboard Cite

show abstract

Detecting Feature Interactions in FORML Models

Cited by 2 publications

References 16 publications

Learning Feature Interactions With and Without Specifications

Learning Feature Interactions With and Without Specifications

Learning feature interactions with and without specifications

Contact Info

Product

Resources

About