A large scale Linux-Kernel based benchmark for feature location research

Xing, Zhenchang; Xue, Yinxing; Jarzabek, Stan

doi:10.1109/icse.2013.6606705

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…The input for the feature location task presented in Section 4 might be considered few input information if we compare it with concern location in maintenance tasks where it is a common practice to trace bug reports with the names and comments in the source code. However, in similar cases to our context of feature-based variants (e.g., the Linux-Kernel benchmark for feature location [26]), we can see that, similarly to EFLBench, only feature names and descriptions are used as input. In this benchmark the feature location task is at a granularity of classes or code fragments, however in our case, it is at the coarse granularity of plugins where we only provide the plugin names as input.…”

Section: Limitations and Threats To Validitymentioning

confidence: 77%

“…This definition is very general and open to interpretation so one recurrent challenge in implementing SPLs is deciding the granularity that the features will have at the implementation level [18]. Coarse granularity (e.g., components or plugins [19,20,21,22,23,24]) makes easier the maintenance of the SPL while fine granularity (e.g., source code classes or code fragments [25,26]) might complicate the development and maintenance of the SPL. This way, there are very diverse scenarios regarding the granularity of the reusable assets in the SPLs.…”

Section: Background On Feature Location In Feature-based Variantsmentioning

confidence: 99%

“…For instance, ArgoUML variants have been extensively used [25]. However, none of the presented case studies have been proposed as a benchmark except the variants of the Linux kernel by Xing et al [26]. This benchmark considers twelve variants of the Linux kernel from which a ground truth is extracted with the traceability of more than two thousands features to code parts.…”

Section: Related Workmentioning

confidence: 99%

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Feature location benchmark for extractive software product line adoption research using realistic and synthetic Eclipse variants

Martínez

Ziadi

Papadakis

et al. 2018

Information and Software Technology

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Context: It is common belief that high impact research in software reuse requires assessment in non-trivial, comparable, and reproducible settings. However, software artefacts and common representations are usually unavailable. Also, establishing a representative ground truth is a challenging and debatable subject. Feature location in the context of software families, which is key for software product line adoption, is a research field that is becoming more mature with a high proliferation of techniques. Objective: We present EFLBench, a benchmark and a framework to provide a common ground for the evaluation of feature location techniques in families of systems. Method: EFLBench leverages the efforts made by the Eclipse Community which provides feature-based family artefacts and their plugin-based implementations. Eclipse is an active and non-trivial project and thus, it establishes an unbiased ground truth which is realistic and challenging. Results: EFLBench is publicly available and supports all tasks for feature location techniques integration, benchmark construction and benchmark usage. We demonstrate its usage, simplicity and reproducibility by comparing four techniques in Eclipse releases. As an extension of our previously published work, we consider a decade of Eclipse releases and we also contribute an approach to automatically generate synthetic Eclipse variants to benchmark feature location techniques in tailored settings. We present and discuss three strategies for this automatic generation and we present the results using different settings. Conclusion: EFLBench is a contribution to foster the research in feature location in families of systems providing a common framework and a set of baseline techniques and results.

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Section: Limitations and Threats To Validitymentioning

confidence: 77%

Section: Background On Feature Location In Feature-based Variantsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Feature location benchmark for extractive software product line adoption research using realistic and synthetic Eclipse variants

Martínez

Ziadi

Papadakis

et al. 2018

Information and Software Technology

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“…For instance, ArgoUML variants [8] have been extensively used. However, none of the presented case studies have been proposed as a benchmark except the variants of the Linux kernel by Xing et al [32]. This benchmark considers 12 variants of the Linux kernel from which a ground truth is extracted with the traceability of 2400 features to code parts.…”

Section: Related Workmentioning

confidence: 99%

Feature Location Benchmark for Software Families Using Eclipse Community Releases

Martínez¹,

Ziadi²,

Papadakis³

et al. 2016

Lecture Notes in Computer Science

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Abstract. It is common belief that high impact research in software reuse requires assessment in realistic, non-trivial, comparable, and reproducible settings. However, real software artefacts and common representations are usually unavailable. Also, establishing a representative ground truth is a challenging and debatable subject. Feature location in the context of software families is a research field that is becoming more mature with a high proliferation of techniques. We present EFLBench, a benchmark and a framework to provide a common ground for this field. EFLBench leverages the efforts made by the Eclipse Community which provides real feature-based family artefacts and their implementations. Eclipse is an active and non-trivial project and thus, it establishes an unbiased ground truth. EFLBench is publicly available and supports all tasks for feature location techniques integration, benchmark construction and benchmark usage. We demonstrate its usage and its simplicity and reproducibility by comparing four techniques.

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“…In the past, this kind of exploration already culminated in realistic benchmarks (e.g. Linux kernel variants [19] or Eclipse releases [15]). …”

Section: Introductionmentioning

confidence: 99%

Mining families of android applications for extractive SPL adoption

Martínez

Ziadi³

et al. 2016

Proceedings of the 20th International Systems and Software Product Line Conference

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The myriads of smart phones around the globe gave rise to a vast proliferation of mobile applications. These applications target an increasing number of user profiles and tasks. In this context, Android is a leading technology for their development and on-line markets are the main means for their distribution. In this paper we motivate, from two perspectives, the mining of these markets with the objective to identify families of apps variants in the wild. The first perspective is related to research activities where building realistic case studies for evaluating extractive SPL adoption techniques are needed. The second is related to a largescale, world-wide and time-aware study of reuse practice in an industry which is now flourishing among all others within the software engineering community. This study is relevant to assess potential for SPLE practices adoption. We present initial implementations of the mining process and we discuss analyses of variant families.

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A large scale Linux-Kernel based benchmark for feature location research

Cited by 7 publications

References 8 publications

Feature location benchmark for extractive software product line adoption research using realistic and synthetic Eclipse variants

Feature location benchmark for extractive software product line adoption research using realistic and synthetic Eclipse variants

Feature Location Benchmark for Software Families Using Eclipse Community Releases

Mining families of android applications for extractive SPL adoption

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