Automatic Extraction of Features and Generation of Feature Models from Java Programs

Damaševičius, Robertas; Paškevičius, Paulius; Karčiauskas, Eimutis; Marcinkevicius, Romas

doi:10.5755/j01.itc.41.4.1108

Cited by 9 publications

(8 citation statements)

References 41 publications

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“…The sixth scenario was extracted from [Chen et al 2005] In this scenario, only the clustering technique was used, making it the most common retrieval technique up to this point. The seventh scenario was defined by extracting the information from [Paškevičius et al 2012]. The clustering and dependency analysis techniques were present once again.…”

Section: Scenario's Resultsmentioning

confidence: 99%

“…Despite the great number of studies on the topic of SPL reengineering [Assunc ¸ão et al 2017, Laguna andCrespo 2013], we observed limitations that motivated us to propose PAxSPL. Firstly, the studies in the literature mostly focus on technical aspects of the variants, namely identification of commonalities and variabilities among variants implementation [Eyal-Salman et al 2013, Paškevičius et al 2012, without taking into account organizational aspects, e.g., team experience. Considering only technical aspects hampers the application of the SPL reengineering process in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PAxSPL: A Generic Framework to Support the Planning of SPL Reengineering

Marchezan¹

2021

Anais Estendidos Do XII Congresso Brasileiro De Software: Teoria E Prática (CBSoft Estendido 2021)

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Extractive Software Product Line (SPL) is a well-known approach that organizations can use to transform their legacy applications into an SPL. In this sense, the SPL reengineering process emerges as a possible strategy for applying the extractive SPL. Available artifacts used to perform the SPL reengineering may change, requiring software engineers to adapt their approaches as a means to satisfying the companies' scenarios. However, there is a lack of an approach supporting this adaptation considering different scenarios. To address these limitations we propose the Prepare, Assemble, and Execute Framework SPL reengineering (PAxSPL). PAxSPL is composed of three different aspects: a process, guidelines, and a supporting tool. For evaluating PAxSPL, we extracted eight different scenarios from the literature. Results evidenced that PAxSPL is customizable to a variety of scenarios with different reengineering artifacts, techniques, and activities.

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Section: Scenario's Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PAxSPL: A Generic Framework to Support the Planning of SPL Reengineering

Marchezan¹

2021

Anais Estendidos Do XII Congresso Brasileiro De Software: Teoria E Prática (CBSoft Estendido 2021)

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“…Reverse engineering [31,34,36,39,48,54,56,70,86,126,127,131,134,144,148,149,151,157,163,164,180,181,191,215,233,244,268] 27…”

Section: Variability Contextmentioning

confidence: 99%

“…Citations #papers Opinion Paper [66,73,75,144,164,193,248] 7 Philosophical Paper [51,74,99,101,134,137,171,175,196,201,244] 11 Solution Proposal [30,35,44,58,60,65,72,78,79,80,81,93,102,106,140,141,142,145,151,158,162,165,172,176,184,185,191,195,197,198,199,200,203,…”

Section: Research Facetmentioning

confidence: 99%

Automated analysis of feature models: Quo vadis?

et al. 2018

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Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.

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“…Feature models (FM) are widely used to describe variability among applications for a given domain, by specifying features and their interdependencies. A feature is typically referred to as an increment in functionality [10,11] and the following relationships among features are supported (see Fig. 1a): mandatory features-child features that must always appear with their parents; optional features-child features that optionally appear with their parents; alternative feature groups-a group of features from which only one can be included in any application; and or feature groups-a group of features that are optional, but at least one needs to be selected.…”

Section: Feature Modelmentioning

confidence: 99%

Multi-Criteria Decision Making for Optimal Configuration of Business Process Model Families

Šendelj

Ognjanović

2018

ITC

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Business process model families (BPMF) provide collective representation of business processes that can be defined for a target domain of interest. They enable rapid development of appropriate business processes by setting up quick BPMF configuration based on the stakeholders' needs and preferences. In this paper, we propose a novel framework for user-centric quality-driven configuration of business process model families capturing different kinds of non-functional preferences and solving the optimal BPMFs configuration problem. Focusing on the following key elements: Analytic Hierarchy Process (AHP), and its extension known as CS-AHP, Fuzzy logic and Genetic Algorithms, the framework maximizes the degree of satisfaction of defined non-functional preferences while simultaneously preserving the behavioural correctness of each derived business process. Furthermore, we provide extensive analyses of the proposed framework from three different perspectives: accuracy assessment, analysis of the impact of different variability and uncertainty elements, and comparison with the published literature indicating that the proposed approach outperforms similar ones in the field.

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Automatic Extraction of Features and Generation of Feature Models from Java Programs

Cited by 9 publications

References 41 publications

PAxSPL: A Generic Framework to Support the Planning of SPL Reengineering

PAxSPL: A Generic Framework to Support the Planning of SPL Reengineering

Automated analysis of feature models: Quo vadis?

Multi-Criteria Decision Making for Optimal Configuration of Business Process Model Families

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