Optimum feature selection in software product lines: Let your model and values guide your search

In Search-Based Software Engineering, well-known metaheuristic search algorithms are utilized to find solutions to common software engineering problems. The algorithms are usually taken "off the shelf" and applied with trust, i.e. software engineers are not concerned with the inner working...

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Bibliographic Details
Published in:2013 1st International Workshop on Combining Modelling and Search-Based Software Engineering (CMSBSE) : proceedings : May 20, 2013, San Francisco, CA, USA pp. 22 - 27
Main Authors: Sayyad, Abdel Salam, Ingram, Joseph, Menzies, Tim, Ammar, Hany
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2013
Subjects:
Evolutionary computation
Feature Models
Indicator-Based Evolutionary Algorithm
Multiobjective Optimization
Optimal Feature Selection
Optimization
Search problems
Search-Based Software Engineering
Software
Software algorithms
Software engineering
Software Product Lines
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Summary:In Search-Based Software Engineering, well-known metaheuristic search algorithms are utilized to find solutions to common software engineering problems. The algorithms are usually taken "off the shelf" and applied with trust, i.e. software engineers are not concerned with the inner workings of algorithms, only with the results. While this may be sufficient is some domains, we argue against this approach, particularly where the complexity of the models and the variety of user preferences pose greater challenges to the metaheuristic search algorithms. We build on our previous investigation which uncovered the power of Indicator-Based Evolutionary Algorithm (IBEA) over traditionally-used algorithms (such as NSGA-II), and in this work we scrutinize the time behavior of user objectives subject to optimization. This analysis brings out the business perspective, previously veiled under Pareto-collective gauges such as Hypervolume and Spread. In addition, we show how slowing down the rates of crossover and mutation can help IBEA converge faster, as opposed to following the higher rates used in many other studies as "rules of thumb".
DOI:10.1109/CMSBSE.2013.6604432