Enhancing Unsupervised Requirements Traceability with Sequential Semantics

Chen, Lei; Wang, Dandan; Wang, Junjie; Wang, Qing

doi:10.1109/apsec48747.2019.00013

Cited by 13 publications

(6 citation statements)

References 27 publications

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“…Study reference Title PS36 [53] Learning effective query transformations for enhanced requirements trace retrieval PS37 [65] Towards a generic framework for requirements traceability management for SysML language PS38 [66] Enhancing unsupervised requirements traceability with sequential semantics PS39 [54] Requirements Tracing on Target (RETRO) enhanced with an automated thesaurus builder PS40 [67] Exploring traceability links via issues for detailed requirements coverage reports PS41 [55] Tracing requirements to tests with high precision and recall PS42 [46] A context-based information retrieval technique for recovering use-case-to-source-code trace links in embedded software systems PS43 [68] Automatically tracing dependability requirements via term-based relevance feedback PS44 [98] A study to support agile methods through traceability PS45 [56] Ontology-based trace retrieval PS46 [69] An ontology-based multi-agent system to support requirements traceability in multi-site software development environment PS47 [100] An ontology-based approach to support for requirements traceability in agile development PS48 [101] Towards requirements reuse by implementing traceability in agile development PS49 [74] An empirical study on project-specific traceability strategies PS50 [80] Evolving software trace links between requirements and source code PS51 [79] Successful deployment of requirements traceability in a commercial engineering organization… really PS52 [75] Motivation matters in the traceability trenches PS53 [19] Towards end-to-end traceability: Insights and implications from five case studies PS54 [83] Traceability-based change awareness PS55 [23] Lightweight traceability for the agile architect PS56 [82] Wolf: Supporting impact analysis activities in distributed software development PS57 [81] Automatic traceability maintenance via machine learning classification PS58 [95] A quality model for the systematic assessment of requirements traceability PS59 [21] A streamlined, cost-effective database approach to manage requirements traceability PS60 [57] A comparative evaluation of two user-feedback techniques for requirements trace retrieval PS61 …”

Section: Idmentioning

confidence: 99%

What have we learnt from the challenges of (semi‐) automated requirements traceability? A discussion on blockchain applicability

2021

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Over the last 3 decades, researchers have attempted to shed light into the requirements traceability problem by introducing tracing tools, techniques, and methods with the vision of achieving ubiquitous traceability. Despite the technological advances, requirements traceability remains problematic for researchers and practitioners. This study aims to identify and investigate the main challenges in implementing (semi-)automated requirements traceability, as reported in the recent literature. A systematic literature review was carried out based on the guidelines for systematic literature reviews in software engineering, proposed by Kitchenham. We retrieved 4530 studies by searching five major bibliographic databases and selected 70 primary studies. These studies were analysed and classified according to the challenges they present and/or address. Twenty-one challenges were identified and were classified into five categories. Findings reveal that the most frequent challenges are technological challenges, in particular, low accuracy of traceability recovery methods. Findings also suggest that future research efforts should be devoted to the human facet of tracing, to explore traceability practices in organisational settings, and to develop traceability approaches that support agile and DevOps practices. Finally, it is recommended that researchers leverage blockchain technology as a suitable technical solution to ensure the trustworthiness of traceability information in interorganisational software projects.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Idmentioning

confidence: 99%

What have we learnt from the challenges of (semi‐) automated requirements traceability? A discussion on blockchain applicability

2021

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“…In order to take semantics into account other approaches use topic models [13] or word embeddings [10], [11], [14] but miss the opportunity to utilize fine-grained relations between artifacts. Instead, they create coarse-grained representations of artifacts, mostly by combining all word embeddings of the resp.…”

Section: Flow Of Eventsmentioning

confidence: 99%

“…Their approach WQI uses the cosine similarity and a learning-to-rank technique. Chen et al [14] use document embeddings in combination with sequential semantics to incorporate sequential information as well. Their approach S2Trace is unsupervised.…”

Section: Related Workmentioning

confidence: 99%

“…We use all datasets introduced in subsection V-A except for eAnci, as we had no access to detailed results of unsupervised approaches on eAnci to compare to. We choose the approaches WQI [11] and S2Trace [14], as they also use embeddings for traceability link recovery. Additionally, we compare our results to the first stage results of COMET [9], as its unsupervised approach performs well on unseen projects 4 .…”

Section: Rq3: Comparison To State-of-the-artmentioning

confidence: 99%

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Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations

Hey

Chen

Weigelt

et al. 2021

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Traceability information is a fundamental prerequisite for many essential software maintenance and evolution tasks, such as change impact and software reusability analyses. However, manually generating traceability information is costly and error-prone. Therefore, researchers have developed automated approaches that utilize textual similarities between artifacts to establish trace links. These approaches tend to achieve low precision at reasonable recall levels, as they are not able to bridge the semantic gap between high-level natural language requirements and code.We propose to overcome this limitation by leveraging finegrained, method and sentence level, similarities between the artifacts for traceability link recovery. Our approach uses word embeddings and a Word Mover's Distance-based similarity to bridge the semantic gap. The fine-grained similarities are aggregated according to the artifacts structure and participate in a majority vote to retrieve coarse-grained, requirement-to-class, trace links.In a comprehensive empirical evaluation, we show that our approach is able to outperform state-of-the-art unsupervised traceability link recovery approaches. Additionally, we illustrate the benefits of fine-grained structural analyses to word embeddingbased trace link generation.

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“…However, manual retrieval of traceability links can be error-prone [4] and timeconsuming. Therefore, automatic retrieval techniques that utilize tools such as information retrieval [5], ontology, machine learning [6], and deep learning [7] are often employed. Deep learning approaches can be classified into two main categories: supervised learning and unsupervised learning [8].…”

Section: Introductionmentioning

confidence: 99%

An Enhancing Recovery Links between Two Artifacts Using Variational Autoencoder

2024

IJIES

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Traceability link recovery is a crucial task in software engineering that ensures the development of dependable and credible software systems. Traceability links between requirements and source code support various activities in the software development process, including change management and software maintenance. These links can be established manually or automatically. Manual trace retrieval is a time-consuming task. Automatic trace retrieval can be performed via various tools such as information retrieval or machine learning methods. Some automatic tools couldn't retrieve the links between requirements and source code. Meanwhile, a big concern associated with automated trace retrieval is the low precision problem primarily caused by the term mismatches across documents to be traced. This study proposes an approach that addresses the low precision problem caused by the term mismatch problem between requirements and source code to obtain the greatest improvements in trace retrieval accuracy. The proposed approach utilizes a variational autoencoder (VAE), an unsupervised deep-learning model in the automated trace retrieval process. We have conducted a series of experiments on three datasets: eTour, SMOS, and eANCI to evaluate our approach against existing approaches. In order to validate the effectiveness of our proposed approach, we compared it to three previous studies that addressed the same problem and utilized the same datasets: the first study used unsupervised machine learning based on clustering, the second study used active learning, and the third study used a classification machine learning. The results show that our proposed approach improves the trace retrieval precision in the automated trace retrieval process.

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Enhancing Unsupervised Requirements Traceability with Sequential Semantics

Cited by 13 publications

References 27 publications

What have we learnt from the challenges of (semi‐) automated requirements traceability? A discussion on blockchain applicability

What have we learnt from the challenges of (semi‐) automated requirements traceability? A discussion on blockchain applicability

Improving Traceability Link Recovery Using Fine-grained Requirements-to-Code Relations

An Enhancing Recovery Links between Two Artifacts Using Variational Autoencoder

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