Leveraging Feature Similarity for Earlier Detection of Unwanted Feature Interactions in Evolving Software Product Lines

Khoshmanesh, Seyedehzahra; Lutz, Robyn R.

doi:10.1007/978-3-030-32047-8_26

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Recent studies show that a new feature similar to an existing feature involved in an existing feature interaction tends to behave similarly [34,35,33]. Therefore, we want to know if having information about existing unwanted feature-interaction paths helps predict new unwanted feature interactions.…”

Section: Rq1f: How Effective Is Finch In Classifying Paths In New Pro...mentioning

confidence: 99%

Learning Feature Interactions With and Without Specifications

Khoshmanesh,

Yavuz,

Lutz

2021

Preprint

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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.

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Section: Rq1f: How Effective Is Finch In Classifying Paths In New Pro...mentioning

confidence: 99%

Learning Feature Interactions With and Without Specifications

Khoshmanesh,

Yavuz,

Lutz

2021

Preprint

Self Cite

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“…Fig. 2 shows the intuition behind how using similarity indexes between two nodes in an interaction graph helps to detect new or missing unwanted feature interactions in a new version or product-line product [18][19][20][21]. As shown in Fig.…”

Section: Overviewmentioning

confidence: 99%

“…This is because similar features have been observed to behave in similar ways. If there is a feature in the feature interaction graph that contributes to some unwanted feature interactions, the features which are similar to this feature often will contribute to the same unwanted interactions [19][20][21].…”

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confidence: 99%

“…The work described in this paper is part of our ongoing effort toward improved detection of feature interactions during the requirements analysis of a new product. In earlier work we studied similarity measures based on features' structural elements (classes, attributes and methods) [19] and on features' relative positions in a software product line's feature model [19][20][21]. New work that is first reported in this paper is our representation of the feature interaction problem as a link prediction problem, which makes feature interactions amenable to classification (wanted/unwanted) and the building of a predictive learning model, together with the evaluation results from our initial application of this approach.…”

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Does Link Prediction Help Detect Feature Interactions in Software Product Lines (SPLs)?

Khoshmanesh,

Lutz

2020

Preprint

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An ongoing challenge for the requirements engineering of software product lines is to predict whether a new combination of features (units of functionality) will create an unwanted or even hazardous feature interaction. We thus seek to improve and automate the prediction of unwanted feature interactions early in development. In this paper we show how the detection of unwanted feature interactions in a software product line can be effectively represented as a link prediction problem. Link prediction uses machine learning algorithms and similarity scores among a graph's nodes to identify likely new edges. We here model the software product line features as nodes and the unwanted interactions among the features as edges. We investigate six link-based similarity metrics, some using local and some using global knowledge of the graph, for use in this context. We evaluate our approach on a software product line benchmark in the literature, building six machine-learning models from the graph-based similarity data. Results show that the best ML algorithms achieved accuracy of 0.75 to 1 for classifying feature interactions as unwanted or wanted in this small study, and that global similarity metrics performed better than local similarity metrics. The work shows how link-prediction models can help find missing edges, which represent unwanted feature interactions that are undocumented or unrecognized, earlier in development.

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Does Link Prediction Help Find Feature Interactions in Software Product Lines?

Khoshmanesh

Lutz

2020

2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE)

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Leveraging Feature Similarity for Earlier Detection of Unwanted Feature Interactions in Evolving Software Product Lines

Cited by 3 publications

References 17 publications

Learning Feature Interactions With and Without Specifications

Learning Feature Interactions With and Without Specifications

Does Link Prediction Help Detect Feature Interactions in Software Product Lines (SPLs)?

Does Link Prediction Help Find Feature Interactions in Software Product Lines?

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