Fragment retrieval on models for model maintenance: Applying a multi-objective perspective to an industrial case study

Pérez, Fernánda; Lapeña, Raúl; Font, Jaime; Cetina, Carlos

doi:10.1016/j.infsof.2018.06.017

Cited by 14 publications

(22 citation statements)

References 17 publications

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“…Many research had done to address the FL problem. Some research using a fragment model to find the feature which tackled model driven development [1]. Another FL research used crowd-based screen cast to help programmers find a sample of code from video tutorial [2].…”

Section: Introductionmentioning

confidence: 99%

Maintenance Web Based Applications Using Feature Location

Arwan

Rusdianto

2020

JITeCS

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Maintenance web applications are a complex set of efforts. The FilkomApps are the web application used by the Faculty of Computer Science of Universitas Brawijaya to arrange the academic, theses of students, assignment of faculty, inventory, presence, honorarium. It has about 6K number of files(HTML, PHP, JS, CSS). The feature location was able to help the maintenance of the web applications by locating specific features on the files. The process comprises of preprocessing (tokenizing, web language syntax removal, splitting, stopword and stemming), indexing (VSM Lucene), and evaluations (precision and recall). The experiments were done by querying the keywords originate from previous maintenance modification effort and feature of a system. The results of precision were 86% and recall were 47%. The precision was better 374% than the conventional method (using the IDE search feature)

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Section: Introductionmentioning

confidence: 99%

Maintenance Web Based Applications Using Feature Location

Arwan

Rusdianto

2020

JITeCS

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“…Considering these numbers, an approach that automatically retrieves model fragments is strongly needed [7]. To address this challenge, we propose a Machine Learning-based approach for fragment retrieval.…”

Section: Querymentioning

confidence: 99%

“…Traceability Link Recovery, Bug Localization, and Feature Location are amongst the most relevant tasks performed during software maintenance [7]. Below there is a brief description of each one:…”

Section: Software Maintenance Tasksmentioning

confidence: 99%

“…A feature is a prominent or distinctive user-visible aspect, quality, or characteristic of a software system [39,40]. The presence or absence of a characteristic in a product, in that sense, entails the existence of different product configurations [7]. In MDD, the feature location is concerned with identifying the elements associated with a specific feature in a product model.…”

Section: Software Maintenance Tasksmentioning

confidence: 99%

“…Therefore, in a model-driven industrial context, companies tend to have a myriad of products with large and complex models behind [7]. In this context, the software engineers have to consume high amounts of time and effort in order to identify the model elements that have to be maintained or evolved.…”

Section: Introductionmentioning

confidence: 99%

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Design of a Machine Learning-based Approach for Fragment Retrieval on Models

Terraza¹,

Cristina²

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In this work, we propose an evolutionary ontological encoding approach to enable Machine Learning techniques to be used to perform Software Engineering tasks in models. The approach is based on a domain ontology to encode a model and on an Evolutionary Algorithm to optimize the encoding. As a result, the encoded model that is returned by the approach can then be used by Machine Learning techniques to perform Software Engineering tasks such as concept location, traceability link retrieval, reuse, impact analysis, etc. We have evaluated the approach with an industrial case study to recover the traceability link between the requirements and the models through a Machine Learning technique (RankBoost). Our results in terms of recall, precision, and the combination of both (F-measure) show that our approach outperforms the baseline (Latent Semantic Indexing). We also performed a statistical analysis to assess the magnitude of the improvement.

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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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Fragment retrieval on models for model maintenance: Applying a multi-objective perspective to an industrial case study

Cited by 14 publications

References 17 publications

Maintenance Web Based Applications Using Feature Location

Maintenance Web Based Applications Using Feature Location

Design of a Machine Learning-based Approach for Fragment Retrieval on Models

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

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