Efficient extraction and analysis of preprocessor-based variability

SinceroJulio,; TartlerReinhard,; Lohmann, Daniel; Schröder-PreikschatWolfgang,

doi:10.1145/1942788.1868300

Cited by 17 publications

(17 citation statements)

References 19 publications

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“…There has been a lot of research directed at studying and ensuring the consistency of the problem and solution spaces [57]. However, most of this work has analyzed features in isolation, either in the problem space [13], [42], [47], [58] or in the solution space [31], [51] to identify modeling practices and feature usage. Some work has also looked at both sides to study co-evolution [34], [41] or to detect bugs due to inconsistencies between models and code [28], [29], [36], [54], [55].…”

Section: Related Workmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

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Abstract-Highly configurable systems allow users to tailor software to specific needs. Valid combinations of configuration options are often restricted by intricate constraints. Describing options and constraints in a variability model allows reasoning about the supported configurations. To automate creating and verifying such models, we need to identify the origin of such constraints. We propose a static analysis approach, based on two rules, to extract configuration constraints from code. We apply it on four highly configurable systems to evaluate the accuracy of our approach and to determine which constraints are recoverable from the code. We find that our approach is highly accurate (93 % and 77 % respectively) and that we can recover 28 % of existing constraints. We complement our approach with a qualitative study to identify constraint sources, triangulating results from our automatic extraction, manual inspections, and interviews with 27 developers. We find that, apart from low-level implementation dependencies, configuration constraints enforce correct runtime behavior, improve users' configuration experience, and prevent corner cases. While the majority of constraints is extractable from code, our results indicate that creating a complete model requires further substantial domain knowledge and testing. Our results aim at supporting researchers and practitioners working on variability model engineering, evolution, and verification techniques.

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Section: Related Workmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

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show abstract

“…For both specifications, we propose novel scalable extraction strategies based on the structural use of #IFDEF directives, on parser and type errors, and on linker checks. Whereas prior work approximated constraints from preprocessor directives [28,42,45,48,55], we design an infrastructure that accurately represents C code based on our previous research on variability-aware parsing and type checking [26,27,30]. In a nutshell, we statically analyze build-time variability effectively without examining an exponential number of configurations.…”

Section: Introductionmentioning

confidence: 99%

Mining configuration constraints: static analyses and empirical results

Nadi

Berger

Kästner

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

112

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Highly-configurable systems allow users to tailor the software to their specific needs. Not all combinations of configuration options are valid though, and constraints arise for technical or non-technical reasons. Explicitly describing these constraints in a variability model allows reasoning about the supported configurations. To automate creating variability models, we need to identify the origin of such configuration constraints. We propose an approach which uses buildtime errors and a novel feature-effect heuristic to automatically extract configuration constraints from C code. We conduct an empirical study on four highly-configurable open-source systems with existing variability models having three objectives in mind: evaluate the accuracy of our approach, determine the recoverability of existing variability-model constraints using our analysis, and classify the sources of variability-model constraints. We find that both our extraction heuristics are highly accurate (93 % and 77 % respectively), and that we can recover 19 % of the existing variability-models using our approach. However, we find that many of the remaining constraints require expert knowledge or more expensive analyses. We argue that our approach, tooling, and experimental results support researchers and practitioners working on variability model re-engineering, evolution, and consistency-checking techniques.

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“…LVAT [35] and Undertaker [10,38,42] are the main examples of such tools. We chose to rely on Undertaker for its convenient wrapping of kconfigdump, allowing us to use the same tools that are also used by the Linux kernel development team.…”

Section: Related Workmentioning

confidence: 99%

Analysing the Linux kernel feature model changes using FMDiff

2015

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Evolving a large scale, highly variable system is a challenging task. For such a system, evolution operations often require to update consistently both their implementation and its feature model. In this context, the evolution of the feature model closely follows the evolution of the system. The purpose of this work is to show that fine-grained feature changes can be used to guide the evolution of the highly variable system. In this paper, we present an approach to obtain fine-grained feature model changes with its supporting tool "FMDiff". Our approach is tailored for Kconfig-based variability models and proposes a feature change classification detailing changes in features, their attributes and attribute values. We apply our approach to the Linux kernel feature model, extracting feature changes occurring in sixteen official releases. In contrast to previous studies, we found that feature modifications are responsible for most of the changes. Then, by taking advantage of the multi-platform aspect of the Linux kernel, we observe the effects of a feature change across the different architecture-specific feature models of the kernel. We found that between 10 and 50 % of feature changes impact all the architecture-specific feature

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Efficient extraction and analysis of preprocessor-based variability

Cited by 17 publications

References 19 publications

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Mining configuration constraints: static analyses and empirical results

Analysing the Linux kernel feature model changes using FMDiff

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