Feature Location in Model-Based Software Product Lines Through a Genetic Algorithm

Font, Jaime; Arcega, Lorena; Haugen, Øystein; Cetina, Carlos

doi:10.1007/978-3-319-35122-3_3

Cited by 27 publications

(34 citation statements)

References 16 publications

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“…In other words, new fragments are generated from existing ones through the use of two genetic operators: the crossover operator, and the mutation operator. We use the crossover and mutation operations presented in [6].…”

Section: Genetic Operations Of the Blim Approachmentioning

confidence: 99%

“…In other words, the model fragment will gain or drop some elements, but the resulting model fragment will still be part of the referenced product model. For more details about these genetic operations see [6].…”

Section: Genetic Operations Of the Blim Approachmentioning

confidence: 99%

“…LSA provides good results when applied to source code [22][16] [21]. We use the LSA technique applied to models in the same way as [6].…”

Section: Model Fragment Similarity To the Bug Descriptionmentioning

confidence: 99%

“…The fitness value that is given to each model fragment is the one that we obtain when we calculate the similarity, obtaining values between -1 and 1. For more details see [6].…”

Section: Model Fragment Similarity To the Bug Descriptionmentioning

confidence: 99%

“…In addition, other approaches use genetic algorithms to locate features in models, Font et al [6,7] propose two approaches to locate features in a model. However, these works do not take into account the modification timespan of the model elements.…”

Section: Related Workmentioning

confidence: 99%

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On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Arcega

Font

Haugen

et al. 2018

Lecture Notes in Information Systems and Organisation

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Bug location is a common task in Software Engineering, specially when maintaining and evolving software products. When locating bugs in code, results depend greatly on the way code modification timespans are weighted. However, the influence of timespan weightings on bug location in models has not received enough attention yet. Throughout this paper, we analyze the influence of several timespan weightings on bug location in models. These timespan weightings guide an evolutionary algorithm, which returns a ranking of model fragments relevant to the solution of a bug. We evaluated our timespan weightings in BSH, a real-world industrial case study, by measuring the results in terms of recall, precision, and F-measure. Results show that the use of the most recent timespan model modifications provide the best results in our study. We also performed a statistical analysis to provide evidence of the significance of the results.

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Section: Genetic Operations Of the Blim Approachmentioning

confidence: 99%

Section: Genetic Operations Of the Blim Approachmentioning

confidence: 99%

“…LSA provides good results when applied to source code [22][16] [21]. We use the LSA technique applied to models in the same way as [6].…”

Section: Model Fragment Similarity To the Bug Descriptionmentioning

confidence: 99%

“…The fitness value that is given to each model fragment is the one that we obtain when we calculate the similarity, obtaining values between -1 and 1. For more details see [6].…”

Section: Model Fragment Similarity To the Bug Descriptionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Arcega

Font

Haugen

et al. 2018

Lecture Notes in Information Systems and Organisation

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Feature location enhancement based on source code augmentation with synonyms of terms

Saifan

Obeidat

2020

Softw Pract Exp

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SummaryIn software maintenance the developers may add new feature to program, improve existing function, and remove bugs. In this case the developer should identify the location in the source code that corresponds to a specific functionality; this is known as feature location. This presents a new approach for enhancing the process of feature location using Information Retrieval (IR) and Natural Language Processing. The approach presented augments the source code with additional semantic information that was extracted and derived from the synonyms of source code terms. This approach works in a pipeline structure, starting by augmenting the source code corpus with synonyms of the original terms and ending by inferring the source code with a particular user query. More specifically, the WordNet platform is used for extracting the synonyms of terms. Moreover, the approach uses an advanced IR technique, namely the Latent Semantic Indexing, for searching and inferring the source code. The used approach was tested and evaluated on two open source systems, namely the Qt and Hippodraw. Four experiments were conducted on each system using 21 features and the results showed that enriching the source code with synonyms of terms clearly and significantly improved the process of feature location efficiently. The experimental results showed that the approach presented obtained higher levels of Recall and Precision. For that reason, it has been shown to improve the state‐of‐the‐art techniques for feature location process.

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On the interaction between the search parameters and the nature of the search problems in search‐based model‐driven engineering

Roca,

Font,

Arcega

et al. 2024

Softw Pract Exp

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The use of search‐based software engineering to address model‐driven engineering activities (SBMDE) is becoming more popular. Many maintenance tasks can be reformulated as a search problem, and, when those tasks are applied to software models, the search strategy has to retrieve a model fragment. There are no studies on the influence of the search parameters when applied to software models. This article evaluates the impact of different search parameter values on the performance of an evolutionary algorithm whose population is in the form of software models. Our study takes into account the nature of the model fragment location problems (MFLPs) in which the evolutionary algorithm is applied. The evaluation searches 1895 MFLPs (characterized through five measures that define MFLPs) from two industrial case studies and uses 625 different combinations of search parameter values. The results show that the impact on the performance when varying the population size, the replacement percentage, or the crossover rate produces changes of around 30% in performance. With regard to the nature of the problems, the size of the search space has the largest impact. Search parameter values and the nature of the MFLPs influence the performance when applying an evolutionary algorithm to perform fragment location on models. Search parameter values have a greater effect on precision values, and the nature of the MFLPs has a greater effect on recall values. Our results should raise awareness of the relevance of the search parameters and the nature of the problems for the SBMDE community.

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Feature Location in Model-Based Software Product Lines Through a Genetic Algorithm

Cited by 27 publications

References 16 publications

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

On the Influence of Modification Timespan Weightings in the Location of Bugs in Models

Feature location enhancement based on source code augmentation with synonyms of terms

On the interaction between the search parameters and the nature of the search problems in search‐based model‐driven engineering

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