Introducing Collaboration for Locating Features in Models: Approach and Industrial Evaluation

Pérez, Fernánda; Marcén, Ana Cristina; Lapeña, Raúl; Cetina, Carlos

doi:10.1007/978-3-319-69462-7_9

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…In this paper, the results are better than without the internal knowledge provided by software engineers. In [44], we obtain a reformulated query from a set of domain experts that serves to evaluate two techniques (Information Retrieval using LSI and Linguistic rules) for locating relevant model fragments. Since LSI obtains the best results in [44], we use LSI in this paper as the feature location technique.…”

Section: Related Workmentioning

confidence: 99%

“…In [44], we obtain a reformulated query from a set of domain experts that serves to evaluate two techniques (Information Retrieval using LSI and Linguistic rules) for locating relevant model fragments. Since LSI obtains the best results in [44], we use LSI in this paper as the feature location technique. In [45], we propose CoFLiM and we evaluate whether collaboration improves the quality of the solution as well as how the quality of the solution is influenced by the number of domain experts input.…”

Section: Related Workmentioning

confidence: 99%

“…CoFLiM only uses Rocchio expansion as the query reformulation technique and it uses a different FL technique (evolutionary algorithm), whereas this paper evaluates the quality of the solution using Rocchio expansion as well as other three query reformulation techniques (RSV expansion, Dice expansion and Query Reduction) using a different FL technique (LSI). In contrast to [44], [45], this paper compares the results with a baseline and explores how the quality of the results is affected by: (1) limiting the time that the internal crowd has to provide knowledge (which is a need to promote internal crowdsourcing in industrial environments), and (2) using different automatic query reformulation techniques.…”

Section: Related Workmentioning

confidence: 99%

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Evaluating Low-Cost in Internal Crowdsourcing for Software Engineering: The Case of Feature Location in an Industrial Environment

et al. 2020

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Internal crowdsourcing in software engineering is a mechanism for recruiting engineers to carry out more efficiently software engineering tasks. However, engineers are busy resources and time is a valuable asset in industry, which hinders internal crowdsourcing in software engineering from becoming a widespread practice. In this work, we propose a low-cost variant of internal crowdsourcing for locating features in models, which limits the time that engineers can spend for providing knowledge. Our approach uses the knowledge provided by the internal crowd to automatically reformulate an initial feature description. The result is taken as input to automatically locate the relevant model fragment using Latent Semantic Indexing. We evaluate our approach using four query reformulation techniques in a real-world case study from our industrial partner. We compare the results of our approach in terms of recall, precision and F-measure with a baseline by means of statistical methods to show that the impact of the results of our approach is significant. Despite the limitation of time, the results show that low-cost in internal crowdsourcing improves significantly the results in an industrial context where engineers' availability is scarce.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Evaluating Low-Cost in Internal Crowdsourcing for Software Engineering: The Case of Feature Location in an Industrial Environment

et al. 2020

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“…It is noteworthy that we have collaborated with other researchers to address some problems which are related to our research. Therefore, although the main contributions of this dissertation are presented in [13,9,14], [15] and [16] also present relevant contributions for this dissertation. [15] extends existing Feature Location approaches based on Information Retrieval and Linguistic rules to locate features in models.…”

Section: Dissertation Overviewmentioning

confidence: 99%

“…Therefore, although the main contributions of this dissertation are presented in [13,9,14], [15] and [16] also present relevant contributions for this dissertation. [15] extends existing Feature Location approaches based on Information Retrieval and Linguistic rules to locate features in models. In this work, our contribution was mainly related to apply the Linguistic Rule-Based approach in railway domain, which is fundamental to answer the RQ5 in this dissertation (see Chapter 7).…”

Section: Dissertation Overviewmentioning

confidence: 99%

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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