Configuring Latent Semantic Indexing for Requirements Tracing

Eder, Sebastian; Femmer, Henning; Hauptmann, Benedikt; Junker, Maximilian

doi:10.1109/ret.2015.13

Cited by 14 publications

(8 citation statements)

References 21 publications

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“…De Lucia et al [11] present a Traceability Link Recovery method and tool based on LSI in the context of an artifact management system, which includes models. The work in [92] takes in consideration the possible configurations of LSI when using the technique for TLR between requirements artifacts, namely requirements and test cases. In their work, the authors state that the configurations of LSI depend on the datasets used, and they expect to be able to determine automatically an appropriate configuration for LSI for any given dataset.…”

Section: Traceability Link Recoverymentioning

confidence: 99%

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Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

Marcén

Lapeña

Pastor

et al. 2020

Journal of Systems and Software

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Section: Traceability Link Recoverymentioning

confidence: 99%

“…In our work, we do not use LSI to perform TLR; instead, we use it as a baseline for comparison with our main line of work, TLR between requirements and models. In addition, we do not tackle different LSI configurations or how LSI configurations impact the results of TLR between requirements and models as [92] does.…”

Section: Traceability Link Recoverymentioning

confidence: 99%

Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

Marcén

Lapeña

Pastor

et al. 2020

Journal of Systems and Software

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“…Moreover, its setting depends on dataset, which significantly affects the accuracy of results. Eder et al (2015) proposed an automatic approach for setting the variable (attributes) of LSI for retrieving traceability links between requirements artefacts. They declared that LSI is an approved technique in information retrieval which is used for tracing links between requirements and software source codes.…”

Section: Related Workmentioning

confidence: 99%

Mining traces between source code and textual documents

Rasekh

Fakhrahmad

Sadreddini

2019

IJCAT

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Currently, researchers in computer science are dealing with a major challenge to link the source codes with the software documents. Writing informal documents by using natural and unstructured language causes this problem. In this paper, we present a model for recovery of traceable links between the source code and requirement documents. The proposed method in this paper is executed in four interconnected sections. The first section goes through extracting the features from the documents, which is followed by extracting the features from the source code. During the third section, abbreviations will be completed, using similarity measure as a feature. Finally, data mining algorithms will be implemented to find the hidden links between source code and software documentation. The most outstanding advantage of using this method is to be independent from the language. Also the preliminary results show that the proposed method has a good performance.

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“…De Lucia et al [26] present a tool based on LSI in the context of an artifact management system. [27] takes in consideration the possible configurations of LSI when using the technique for TLR between Requirements artifacts. In their work, the authors state that the configurations of LSI depend on the datasets used, and they look forward to automatically determining an appropriate configuration for LSI for any given dataset.…”

Section: Threats To Validitymentioning

confidence: 99%

Exploring New Directions in Traceability Link Recovery in Models: The Process Models Case

Lapeña

Font

Cetina

et al. 2018

Advanced Information Systems Engineering

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Traceability Links Recovery (TLR) has been a topic of interest for many years. However, TLR in Process Models has not received enough attention yet. Through this work, we study TLR between Natural Language Requirements and Process Models through three different approaches: a Models specific baseline, and two techniques based on Latent Semantic Indexing, used successfully over code. We adapted said code techniques to work for Process Models, and propose them as novel techniques for TLR in Models. The three approaches were evaluated by applying them to an academia set of Process Models, and to a set of Process Models from a real-world industrial case study. Results show that our techniques retrieve better results that the baseline Models technique in both case studies. We also studied why this is the case, and identified Process Models particularities that could potentially lead to improvement opportunities.

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Configuring Latent Semantic Indexing for Requirements Tracing

Cited by 14 publications

References 21 publications

Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

Traceability Link Recovery between Requirements and Models using an Evolutionary Algorithm Guided by a Learning to Rank Algorithm: Train control and management case

Mining traces between source code and textual documents

Exploring New Directions in Traceability Link Recovery in Models: The Process Models Case

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