Software Configuration Engineering in Practice Interviews, Survey, and Systematic Literature Review

Sayagh, Mohammed; Kerzazi, Noureddine; Adams, Bram; Petrillo, Fábio

doi:10.1109/tse.2018.2867847

Cited by 35 publications

(46 citation statements)

References 82 publications

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“…Based on our research methodology, we iteratively explore more dimensions and values by taking related interviews and survey answers of the two studies (Section 2.2) into account. Due to space limitations, we report only on the final model (not intermediate steps), but highlight at the beginning of each dimension the study from which their values originate with the following subscript: our study 0 , [5] 1 , and [34] 2 . To give an overview of our model, we summarize the origin of the dimensions of configurations and their values from the different sources of our analysis in Figure 1.…”

Section: Results: a Model On Configurationmentioning

confidence: 99%

“…Study 2: Software Configuration Engineering in Practice. Sayagh and others conducted a qualitative study about the engineering process of run-time configuration options in industry [34]. They identify challenges and best practices about creating, maintaining, and using configuration options across different software artifacts.…”

Section: Studymentioning

confidence: 99%

“…Software configuration is a hot topic in research and industry [34]. Despite its importance, there are many different views about what aspects comprise configuration and how they interact.…”

Section: Introductionmentioning

confidence: 99%

“…Despite its importance, there are many different views about what aspects comprise configuration and how they interact. For example, in combinatorial testing [19,25], configuration is usually seen as a set of input variables and parameters to a program that needs to be tested; in software product lines [2,7], configuration corresponds to a selection of features or configuration options for generating a program variant with a desired functional behavior; in optimization [32,38], the set of configuration options and parameters are regarded as configuration for optimizing non-functional properties; and in the deployment process [34], configuration is a means to define where, when, what, and how to deploy software artifacts.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dimensions of software configuration: on the configuration context in modern software development

Siegmund¹,

Ruckel

Siegmund

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Section: Results: a Model On Configurationmentioning

confidence: 99%

Section: Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dimensions of software configuration: on the configuration context in modern software development

Siegmund¹,

Ruckel

Siegmund

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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“…According to an interview study with practitioners [7] there are three common use cases for feature flags: (a) parallel trunk-based development, where multiple features guarded by feature flags are developed simultaneously in the same branch, (b) canary releases, where features are released incrementally to different users, and (c) experimentation in production (A/B testing), where features are selectively activated to measure their impact on business objectives. In addition, they found that the boundary between feature flags (intended to be temporary) and configuration options (intended to be permanent) [11] is often fuzzy, and often the same mechanisms are used for both. Academic research on feature flags is rather sparse.…”

Section: Related Work On Feature Flagsmentioning

confidence: 99%

Capture the Feature Flag

Meinicke

Hoyos

Vasilescu

et al. 2020

Proceedings of the 17th International Conference on Mining Software Repositories

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Feature flags (a.k.a feature toggles) are a mechanism to keep new features hidden behind a boolean option during development. Flags are used for many purposes, such as A/B testing and turning off a feature more easily in case of failures. While software engineering research on feature flags is burgeoning, examples of software projects using flags rarely come from outside commercial and private projects, stifling academic progress. To address this gap, in this paper we present a novel semi-automated mining software repositories approach to detect feature flags in open-source projects, based on analyzing the projects' commit messages and other project characteristics. With our approach, we search over all open-source GitHub projects, finding multiple thousand plausible and active candidate feature flagging projects. We manually validate projects and assemble a dataset of 100 confirmed feature flagging projects. To demonstrate the benefits of our detection technique, we report on an initial analysis of feature flags in the validated sample of 100 projects, investigating practices that correlate with shorter flag lifespans (typically desirable to reduce technical debt), such as using the issue tracker and having a flag owner.

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Scratching the Surface of ./configure: Learning the Effects of Compile-Time Options on Binary Size and Gadgets

Tërnava

Acher

Lesoil

et al. 2022

Reuse and Software Quality

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Numerous software systems are configurable through compiletime options and the widely used ./configure. However, the combined effects of these options on binary's non-functional properties (size and attack surface) are often not documented, and or not well understood, even by experts. Our goal is to provide automated support for exploring and comprehending the configuration space (a.k.a., surface) of compile-time options using statistical learning techniques. In this paper, we perform an empirical study on four C-based configurable systems. We measure the variation of binary size and attack surface (by quantifying the number of code reuse gadgets) in over 400 compile-time configurations of a subject system. We then apply statistical learning techniques on top of our build infrastructure to identify how compile-time options relate to non-functional properties. Our results show that, by changing the default configuration, the system's binary size and gadgets vary greatly (roughly −79% to 244% and −77% to 30%, respectively). Then, we found out that identifying the most influential options can be accurately learned with a small training set, while their relative importance varies across size and attack surface for the same system. Practitioners can use our approach and artifacts to explore the effects of compile-time options in order to take informed decisions when configuring a system with ./configure.

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Software Configuration Engineering in Practice Interviews, Survey, and Systematic Literature Review

Cited by 35 publications

References 82 publications

Dimensions of software configuration: on the configuration context in modern software development

Dimensions of software configuration: on the configuration context in modern software development

Capture the Feature Flag

Scratching the Surface of ./configure: Learning the Effects of Compile-Time Options on Binary Size and Gadgets

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