Feature-oriented software evolution

Passos, Leonardo; Czarnecki, Krzysztof; Apel, Sven; Wąsowski, Andrzej; Kästner, Christian; Guo, Jianmei

doi:10.1145/2430502.2430526

Cited by 55 publications

(28 citation statements)

References 46 publications

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“…Furthermore, in the work of Neves et al on the safe evolution of SPLs [25], we can observe that the change scenarios described in this work intertwine evolution of the variability model and its implementation. Finally, in [28], Passos et al envision that adopting a feature-oriented view on software evolution could enable easier traceability, analysis and generally facilitate evolution management. All of those studies comfort us in the idea that feature evolution is tightly coupled to the evolution of its associated product line, and as a consequence that the evolution of the feature model reflects the evolution of the product line as a whole, the main idea behind of our study.…”

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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Section: Related Workmentioning

confidence: 99%

Analysing the Linux kernel feature model changes using FMDiff

2015

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“…In addition to the change activities defmed in the change mini cycle [12], in software product lines we must identify if the change is local (at the product level) or must be propagated to the reference architecture and estimate the impact of this change on the other products. The change impact analysis task in the context of software product lines is crucial [13] because if not well studied it could break the architectural integrity of the entire family. In order to analyse the impact accurately we must have a set of perquisite such as available documentation and knowledge about the software product line, and for each component of the system the network of dependencies with the other components must be clearly defined.…”

Section: Software Product Line Evolution Problematicmentioning

confidence: 99%

“…A feature-oriented evolution approach was proposed by [13] for better change understanding. The approach is based on the hypothesis that managing change at level of features is understood by all stakeholders of a system because of the tight relation between features and requirements.…”

Section: A Traceabilitymentioning

confidence: 99%

Towards a co-evolution model for software product lines based on cladistics

Benlarabi¹

2014

2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS)

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The evolution of a software product line is more challenging than the evolution of a single product, because its artifacts depend on many products. If a change is not well studied, it can break the integrity of the reference architecture of the software product line and all the derived products.Hence, when dealing with evolution, the understanding of change and its impact must be the most important activity.Biology is the science which studied evolution the most.Therefore it was necessary for us to understand evolution in biology in order to handle evolution in software product lines.This paper presents our approach to deal with change understanding in software product line using co-evolution analysis which was used in biology to understand how organisms influence each other during their evolution over time. Similarly to biological co-evolution, the co-evolution of artifacts of a software product line consists on analyzing how the evolution of an artifact influences the evolution of the others. The input data used in our approach are extracted from the history of changes that happened to each version of the software product. Our objective is to establish a co evolution model for software product lines using Cladistics classification which illustrates the evolution of a population through an evolutionary tree.

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“…In a rapidly changing business environment, requirements change continuously to fulfil a user's demand, depending on their location. An organization must have RE that addresses the organization's business policy and manages the requirements according to that policy [7]. Basically, the most commonly followed policy in the software development process is that it should fit the needs of the project [8].…”

Section: Introductionmentioning

confidence: 99%

A Practical Approach for Evaluating and Prioritizing Situational Factors in Global Software Project Development

Gulzar¹,

Sang²,

Memon³

et al. 2018

ijacsa

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Abstract-There has been an enormous increase in globalization that has led to more cooperation and competition across boundaries. Software engineering, particularly distributed software development (DSD) and global software development (GSD), is evolving rapidly and presents several challenges, such as geographical separations, temporal differences, cultural variations, and management strategies. As a result, a variety of situational factors (SFs) arise that causes challenging problems in software development. Both literature and real world software industry study revealed that the extent of the effect of SFs may vary subject to a certain software project. Project executives should need to concentrate on the right SFs for the successful development of a specific project. This work first examines the optimal and most well-balanced GSD-related SFs and then presents a mechanism for prioritizing the SFs to better understand the extent to which an SF generally affects the GSD. A set of 56 SFs in 11 categories is identified and analyzed in this research. A fuzzy set theory based, multi criteria decision making (MCDM) technique, fuzzy analytical hierarchy process (FAHP) was proposed to extract the SFs that have the strongest effects on GSD. The proposed technique is intelligent and automated and can be customized to suit specific conditions and environments. Thus, it can provide support for a much-needed variation that is the hallmark of such software development environments. A case study of a global company working in collaboration on a project JKL was selected to identify and prioritize the most challenging SFs. A sensitivity analysis is carried out to evaluate the extent of the impact for highly ranked SFs related to JKL project.

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Feature-oriented software evolution

Cited by 55 publications

References 46 publications

Analysing the Linux kernel feature model changes using FMDiff

Analysing the Linux kernel feature model changes using FMDiff

Towards a co-evolution model for software product lines based on cladistics

A Practical Approach for Evaluating and Prioritizing Situational Factors in Global Software Project Development

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