The Linux kernel: a case study of build system variability

IIEEE Trans. Software Eng.

Kästner

et al. 2015

Self Cite

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.

Section: Recoverability Discussionmentioning

confidence: 96%

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

Nadi

IIEEE Trans. Software Eng.

Kästner

et al. 2015

Self Cite

“…An interesting case is Linux where already 27 % of the hierarchy constraints are mirrored in the nested directory structure in the build system (i.e., file PCs). We conjecture that this results from the highly nested code structure, where most individual directories and files are controlled by a hierarchy of Makefiles, almost mimicking the variability model hierarchy [12,33]. On the other hand, although harder to recover, cross-tree constraints seem to be scattered across different places in the code (e.g., linker and type information), and seem more related to preventing build errors than hierarchy constraints are.…”

Section: O2: Recoverabilitymentioning

confidence: 98%

“…Our work focuses on C based systems with build-time variability using the build system and C preprocessor. Since many features are directly used in implementation files [33], we assume that many of the configuration constraints are reflected in the code. We design and implement a scalable approach to extract constraints statically.…”

Section: Introductionmentioning

confidence: 99%

Mining configuration constraints: static analyses and empirical results

Nadi

Proceedings of the 36th International Conference on Software Engineering

Kästner

et al. 2014

Self Cite

111

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.

“…No concept for composite units exists. The feature-to-code mapping resides in the build system [46,47]. Debian's basic units are packagesfile archives with helper scripts and a manifest.…”

Section: Variability Representationmentioning

confidence: 99%

Variability mechanisms in software ecosystems

Information and Software Technology

Pfeiffer

Tartler

et al. 2014

Context: Software ecosystems are increasingly popular for their economic, strategic, and technical advantages. Application platforms such as Android or iOS allow users to highly customize a system by selecting desired functionality from a large variety of assets. This customization is achieved using variability mechanisms. Objective: Variability mechanisms are well-researched in the context of software product lines. Although software ecosystems are often seen as conceptual successors, the technology that sustains their success and growth is much less understood. Our objective is to improve the empirical understanding of variability mechanisms used in successful software ecosystems. Method: We analyze five ecosystems, ranging from the Linux kernel through Eclipse to Android. A qualitative analysis identifies and characterizes variability mechanisms together with their organizational context. This analysis leads to a conceptual framework that unifies ecosystem-specific aspects using a common terminology. A quantitative analysis investigates scales, growth rates, and-most importantly-dependency structures of the ecosystems. Results: In all the studied ecosystems, we identify rich dependency languages and variability descriptions that declare many direct and indirect dependencies. Indirect dependencies to abstract capabilities, as opposed to concrete variability units, are used predominantly in fast-growing ecosystems. We also find that variability models-while providing systemwide abstractions over code-work best in centralized variability management and are, thus, absent in ecosystems with large free markets. These latter ecosystems tend to emphasize maintaining capabilities and common vocabularies, dynamic discovery, and binding with strong encapsulation of contributions, together with uniform distribution channels. Conclusion:The use of specialized mechanisms in software ecosystems with large free markets, as opposed to software product lines, calls for recognition of a new discipline-variability encouragement.