• Corpus ID: 13630105

A Hitchhiker's Guide to Search-Based Software Engineering for Software Product Lines

@article{LopezHerrejon2014AHG,
  title={A Hitchhiker's Guide to Search-Based Software Engineering for Software Product Lines},
  author={Roberto Erick Lopez-Herrejon and Javier Ferrer and Francisco Chicano and Lukas Linsbauer and Alexander Egyed and Enrique Alba},
  journal={ArXiv},
  year={2014},
  volume={abs/1406.2823}
}
Search Based Software Engineering (SBSE) is an emerging discipline that focuses on the application of search-based optimization techniques to software engineering problems. The capacity of SBSE techniques to tackle problems involving large search spaces make their application attractive for Software Product Lines (SPLs). In recent years, several publications have appeared that apply SBSE techniques to SPL problems. In this paper, we present the results of a systematic mapping study of such… 
A systematic mapping study of search-based software engineering for software product lines
TLDR
The study identified the need to improve the robustness of the empirical evaluation of existing research, a lack of extensive and robust tool support, and multiple avenues worthy of further investigation.
Search based software engineering for software product line engineering: a survey and directions for future work
TLDR
Suggestions for the development of recent advances in genetic improvement are focused on, showing how these might be exploited by SPL researchers and practitioners.
A first systematic mapping study on combinatorial interaction testing for software product lines
TLDR
The first systematic mapping study on applying Combinatorial Interaction Testing (CIT) for SPL testing is presented, aiming to identify common trends, gaps, and opportunities for further research and application.
Evolutionary Computation for Software Product Line Testing: An Overview and Open Challenges
TLDR
This chapter provides a concise overview of the state of the art and practice in SPL testing with evolutionary techniques as well as to highlight open questions and areas for future research.
An assessment of search-based techniques for reverse engineering feature models
TLDR
Three standard search based techniques with two objective functions on 74 SPLs compared their performance using precision and recall, and found a clear trade-off between these two metrics which was reified into a third objective function based on Fβ, an information retrieval measure, that showed a clear performance improvement.
Comparison of search strategies for feature location in software models
TLDR
The best results overall are obtained by a hybrid between evolutionary algorithm and hill climbing; the size of the search space has the greatest impact on the results obtained by the search strategies; and the impact of each of the measures is not the same in the five search strategies.
Location of Features as Model Fragments and their Co-Evolution
TLDR
This dissertation presents an approach for Feature Location in Models that relies on an Evolutionary Algorithm (FLiMEA) that capitalizes on experts domain knowledge to boost the feature location process and produce model fragments that properly capture the reusable units of the domain.
Achieving Feature Location in Families of Models Through the Use of Search-Based Software Engineering
TLDR
Five search algorithms are proposed and compared to locate features over families of product models guided by latent semantic analysis (LSA), a technique that measures similarities between textual queries.
Search-based product line fault detection allocating test cases iteratively
TLDR
A search-based approach to perform the testing of product lines by allocating small number of test cases in each of the products increases the probability of detecting faults faster and can reduce the fault detection time by 61% and 65% on average when compared with the traditional test process and the Random Search algorithm respectively.
Computational Intelligence and Quantitative Software Engineering
TLDR
This volume consists of contributions written by widely acknowledged experts in the field who reveal how the Software Engineering benefits from the key foundations and synergistically existing technologies of Computational Intelligence being focused on knowledge representation, learning mechanisms, and population-based global optimization strategies.
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