Multi-Objective Optimization of Feature Model in Software Product Line: Perspectives and Challenges

Abbas, Asad; Siddiqui, Isma Farah; Lee, Scott Uk Jin

doi:10.17485/ijst/2016/v9i45/106769

Cited by 9 publications

(3 citation statements)

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“…We studied the largest open source Feature Model we could find [1]: the Linux kernel [51] containing a maximum of 13,322 features and 277,521 constraints in its version 2.6.32 (see Table 1). We evaluated the demographics (features, constraints) and evolution of 21 versions of the kernel from version 2.6.12 to version 2.6.32 that are publicly available in the Linux Variability Analysis Tools (LVAT) repository [52].…”

Section: Study Of the Linux Kernelmentioning

confidence: 99%

“…These data sets are widely used in the literature [1], cover large real-world Feature Models and are available in the LVAT repository [52]. For each of these data sets and for each evolution target, we generate 10 different versions.…”

Section: Study Of the Linux Kernelmentioning

confidence: 99%

“…-We propose a benchmark 1 for the analysis of evolving SPL; this data set has been generated following a study of the demographics and evolution of a large SPL (Linux kernel). This data set is important to provide a good evaluation of the different algorithms under different evolution scenarios;…”

Section: Introductionmentioning

confidence: 99%

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Is seeding a good strategy in multi-objective feature selection when feature models evolve?

Saber

Brevet

Botterweck

et al. 2018

Information and Software Technology

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Context: When software architects or engineers are given a list of all the features and their interactions (i.e., a Feature Model or FM) together with stakeholders' preferences-their task is to find a set of potential products to suggest the decision makers. Software Product Lines Engineering (SPLE) consists in optimising those large and highly constrained search spaces according to multiple objectives reflecting the preference of the different stakeholders. SPLE is known to be extremely skill-and labour-intensive and it has been a popular topic of research in the past years. Objective: This paper presents the first thorough description and evaluation of the related problem of evolving software product lines. While change and evolution of software systems is the common case in the industry, to the best of our knowledge this element has been overlooked in the literature. In particular, we evaluate whether seeding previous solutions to genetic algorithms (that work well on the general problem) would help them to find better/faster solutions. Method: We describe in this paper a benchmark of large scale evolving FMs, consisting of 5 popular FMs and their evolutions-synthetically generated following an experimental study of FM evolution. We then study the performance of a state-of-the-art algorithm for multi-objective FM selection (SATIBEA) when seeded with former solutions. Results: Our experiments show that we can improve both the execution time and the quality of SATIBEA by feeding it with previous configurations. In particular, SATIBEA with seeds proves to converge an order of magnitude faster than SATIBEA alone. Conclusion: We show in this paper that evolution of FMs is not a trivial task and that seeding previous solutions can be used as a first step in the optimisation-unless the difference between former and current FMs is high, where seeding has a limited impact.

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Section: Study Of the Linux Kernelmentioning

confidence: 99%

Section: Study Of the Linux Kernelmentioning

confidence: 99%

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Is seeding a good strategy in multi-objective feature selection when feature models evolve?

Saber

Brevet

Botterweck

et al. 2018

Information and Software Technology

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Multi‐objective optimization‐based and fault localization‐oriented test case generation for novice programs

Liu,

Yang,

Fan

et al. 2024

J Software Evolu Process

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SummaryOnline judgment (OJ) systems are capable of evaluating program results by automatically executing test cases, significantly improving the efficiency of traditional guidance approaches. Moreover, existing studies attempt to assist novices through automated fault localization techniques to provide feedback to novices, which can help them quickly find the location of faulty statements. Among them, spectrum‐based fault localization (SBFL) techniques have been widely used for their lightweight and efficiency, which only requires coverage information and test results of test cases to conduct fault localization. However, manually constructing high‐quality test cases for a large number of OJ questions is tough work to complete. To solve this problem, we propose the novice program‐oriented Multi‐Objective Optimization‐Based Fault Localization‐Oriented Test Case Generation (MFTCG) for automatically generating test inputs. Specifically, we use multi‐objective optimization algorithms to evolve the test case in terms of both fault localization and faulty code detection capability. We conduct experiments with 8911 programs from the well‐known public OJ platform AtCoder. The results show that our proposed approach MFTCG can achieve the best fault localization performance compared with existing automated test case generation approaches in most cases and can achieve the similar faulty code detection capability compared to manually designed test cases.

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Variabilitätsmodellierung und Optimierung softwareintensiver Systeme durch einen Regulator Algorithmus (RGA)

Meyer

2021

Neue Algorithmen Für Praktische Probleme

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Multi-Objective Optimization of Feature Model in Software Product Line: Perspectives and Challenges

Cited by 9 publications

References 0 publications

Is seeding a good strategy in multi-objective feature selection when feature models evolve?

Is seeding a good strategy in multi-objective feature selection when feature models evolve?

Multi‐objective optimization‐based and fault localization‐oriented test case generation for novice programs

Variabilitätsmodellierung und Optimierung softwareintensiver Systeme durch einen Regulator Algorithmus (RGA)

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