An Improved Approach to Traceability Recovery Based on Word Embeddings

Zhao, Teng; Cao, Qinghua; Sun, Qing

doi:10.1109/apsec.2017.14

Cited by 27 publications

(13 citation statements)

References 26 publications

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“…Although not enjoying the immense popularity of Word2Vec, it is still prominent to the scientific community [46,6,39,7]. In requirement traceability, researchers also made use of word embeddings to recover appropriate links [9,45,44]. Our current approach differs from these in many aspects.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Textual Similarity Techniques in Code Level Traceability

Csuvik

Kicsi

Vidács

2019

Computational Science and Its Applications – ICCSA 2019

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Automatic recovery of test-to-code traceability links is an important task in many areas of software engineering, like quality assurance and code maintenance. The research community has shown great interest in such a topic and has developed several techniques that already made significant advances in the field. These techniques include text-based learning algorithms, of which corpus is built from the source code of the software components. Several techniques based on information retrieval have been benchmarked, but the capabilities of many learning algorithms have not yet been tested. In this work we examine the textual similarity measures produced by three different machine learning techniques for the recovery of traceability information while also considering various textual representations of the source code. The obtained results are evaluated on 4 open source systems based on naming conventions. We have been able to improve the current textual similarity based state-of-the-art results in the case of each evaluated system.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Textual Similarity Techniques in Code Level Traceability

Csuvik

Kicsi

Vidács

2019

Computational Science and Its Applications – ICCSA 2019

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“…The distribution of vectors within the space varies depending on the specific approach taken. The use of word embedding has achieved significant success for addressing NLP challenges in domains such as ad-hoc information retrieval [4][51], bug localization [57], question answering [15] and also trace link recovery [58] [20].…”

Section: Word Embedding (We)mentioning

confidence: 99%

Traceability Support for Multi-Lingual Software Projects

Liu

Lin

Cleland‐Huang

2020

Proceedings of the 17th International Conference on Mining Software Repositories

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Software traceability establishes associations between diverse software artifacts such as requirements, design, code, and test cases. Due to the non-trivial costs of manually creating and maintaining links, many researchers have proposed automated approaches based on information retrieval techniques. However, many globally distributed software projects produce software artifacts written in two or more languages. The use of intermingled languages reduces the efficacy of automated tracing solutions. In this paper, we first analyze and discuss patterns of intermingled language use across multiple projects, and then evaluate several different tracing algorithms including the Vector Space Model (VSM), Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA), and various models that combine mono-and cross-lingual word embeddings with the Generative Vector Space Model (GVSM). Based on an analysis of 14 Chinese-English projects, our results show that best performance is achieved using mono-lingual word embeddings integrated into GVSM with machine translation as a preprocessing step.

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“…Nevertheless, traditional feature representation methods often ignore the contextual information or word order in texts and remain unsatisfactory for capturing the semantics of the words. Recent studies in SE [75,74,76,69] began to use pre-trained word embeddings, which can capture rich syntactic and semantic features of words in a low-dimensional vector. For example, Han et al [69] proposed a deep learning approach to predict the severity level of software vulnerability, using a continuous skip-gram model (one of the popular word embedding models) to capture word-level features and a one-layer shallow CNN to capture sentence-level features in vulnerability descriptions.…”

Section: Feature Preparationmentioning

confidence: 99%

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?

Wang,

Huang,

et al. 2020

Preprint

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Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Machine Learning (ML)/Deep Learning (DL) and Software Engineering (SE). Meanwhile, critical reviews have emerged that suggest that ML/DL should be used cautiously. To improve the quality (especially the applicability and generalizability) of ML/DLrelated SE studies, and to stimulate and enhance future collaborations between SE/AI researchers and industry practitioners, we conducted a 10-year Systematic Literature Review (SLR) on 906 ML/DL-related SE papers published between 2009 and 2018. Our trend analysis demonstrated the mutual impacts that ML/DL and SE have had on each other. At the same time, however, we also observed a paucity of replicable and reproducible ML/DL-related SE studies and identified five factors that influence their replicability and reproducibility. To improve the applicability and generalizability of research results, we analyzed what ingredients in a study would facilitate an understanding of why a ML/DL technique was selected for a specific SE problem. In addition, we identified the unique trends of impacts of DL models on SE tasks, as well as five unique challenges that needed to be met in order to better leverage DL to improve the productivity of SE tasks. Finally, we outlined a road-map that we believe can facilitate the transfer of ML/DL-based SE research results into real-world industry practices.

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An Improved Approach to Traceability Recovery Based on Word Embeddings

Cited by 27 publications

References 26 publications

Evaluation of Textual Similarity Techniques in Code Level Traceability

Evaluation of Textual Similarity Techniques in Code Level Traceability

Traceability Support for Multi-Lingual Software Projects

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?

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