Change impact analysis for maintenance and evolution of variable software systems

Angerer, Florian; Grimmer, Andreas; Prähofer, Herbert; Grünbacher, Paul

doi:10.1007/s10515-019-00253-7

Cited by 22 publications

(4 citation statements)

References 53 publications

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“…Every clause can be considered a feature interaction, i.e., a static interaction of the features contained in the clause. This aligns with previous research in feature algebra (Liu et al 2006), feature location (Linsbauer et al 2013), and the analysis of variable systems (Fischer et al 2016;Angerer et al 2019). We denote the set of all conjunctive clauses that can be formed given a set of feature revisions v.F of variant v as clauses(v.F ).…”

Section: Presence Conditionssupporting

confidence: 89%

Evolving software system families in space and time with feature revisions

et al. 2022

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Software companies commonly develop and maintain variants of systems, with different feature combinations for different customers. Thus, they must cope with variability in space. Software companies further must cope with variability in time, when updating system variants by revising existing software features. Inevitably, variants evolve orthogonally along these two dimensions, resulting in challenges for software maintenance. Our work addresses this challenge with ECSEST (Extraction and Composition for Systems Evolving in Space and Time), an approach for locating feature revisions and composing variants with different feature revisions. We evaluated ECSEST using feature revisions and variants from six highly configurable open source systems. To assess the correctness of our approach, we compared the artifacts of input variants with the artifacts from the corresponding composed variants based on the implementation of the extracted features. The extracted traces allowed composing variants with 99-100% precision, as well as with 97-99% average recall. Regarding the composition of variants with new configurations, our approach can combine different feature revisions with 99% precision and recall on average. Additionally, our approach retrieves hints when composing new configurations, which are useful to find artifacts that may have to be added or removed for completing a product. The hints help to understand possible feature interactions or dependencies. The average time to locate feature revisions ranged from 25 to 250 seconds, whereas the average time for composing a variant was 18 seconds. Therefore, our experiments demonstrate that ECSEST is feasible and effective.

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Section: Presence Conditionssupporting

confidence: 89%

Evolving software system families in space and time with feature revisions

et al. 2022

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“…It relies on the feature-to-code mappings from the ECCO system and operates on the code model. Further, the code dependency analysis component is capable of analyzing and representing all code-level control and data dependencies in a system, also considering its variability (Angerer et al, 2014(Angerer et al, , 2019. The FORCE 2 feature dependency analysis component allows analyzing and representing dependencies at the levels of features (Feichtinger et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Feature-oriented clone and pull operations for distributed development and evolution

et al. 2022

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Software companies frequently customize and extend product lines in multiple projects concurrently to quickly deliver solutions to customers. Engineers use a distributed and feature-oriented development process, commonly supported by version control systems to track implementation-level changes. For instance, feature branches are widely used to add new or modify existing features. However, when merging back features to the product line, the information about feature-to-code mappings is usually lost. Furthermore, the granularity of merging is limited to branches, making it hard to extract and merge selected individual features from one product to another. This paper thus presents feature-oriented clone and pull operations for distributed development, which are implemented in the FORCE2 platform. Our evaluation uses variants of the ArgoUML product line to investigate the correctness and performance of our approach. The results show that the feature-oriented operations work with high precision and recall for different cases of feature interactions, also when feature implementations are scattered across many locations in the source code. The performance measurements demonstrate that the operations can be integrated in the typical workflows of engineers.

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“…At present, in the study of product derivation, many studies focus on the realization of the variability of product family members at the implementation level [4,5]. The techniques are code-based export methods that assign features to code [6]. They are usually featureto-artifact mappings [7], e.g., AspectJ for Java or preprocessor annotations [8,9] for text files.…”

Section: Introductionmentioning

confidence: 99%

Product Model Derivation from Feature Model and Formal Specification

Wang

Liu

2022

Applied Sciences

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Product derivation is the process of building a specific product from a software product line. Effective product derivation can improve software reuse productivity. Existing methods can only obtain abstract feature models, lacking detailed specifications of individual features. They are more about deriving code assets or class diagram templates without precise model descriptions for specific products. This article proposes a product derivation approach to obtain a formal specification of a specific product based on the feature model and formal specification. We use the integration ordering and behavior preserving integration techniques to integrate the formal specification for each feature pair. The method is divided into two steps. First, it determines the feature formal specification integration ordering based on the feature model. Then, the behavior-preserving integration will be conducted for pairs, including declaration integration, functional scenario path generation, and function integration based on path matching. Behavior preserving integration guarantees consistent behavior to ensure the quality of the formal specification after integration. Finally, we developed a support tool to conduct a case study. The tool first guides the user to perform feature functional scenario path matching, then performs functional integration based on the matching results and repeats the above steps to generate a product model. The result indicates that our method facilitates the derivation process and improves the quality of the generated models.

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Change impact analysis for maintenance and evolution of variable software systems

Cited by 22 publications

References 53 publications

Evolving software system families in space and time with feature revisions

Evolving software system families in space and time with feature revisions

Feature-oriented clone and pull operations for distributed development and evolution

Product Model Derivation from Feature Model and Formal Specification

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