Recovering Traceability Links between Code and Documentation for Enterprise Project Artifacts

Nagano, Shouichi; Ichikawa, Yusuke; Kobayashi, Takao

doi:10.1109/compsac.2012.10

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…For example, as shown in Fig. 4, Syntax Tree [27] and Refactoring [15][32] [36] are used to reduce the adverse effects caused by inconsistent terminology or missing, misplaced signs in textual artifacts. The details of enhancement strategies for IR-based RTR are listed in Table III.…”

Section: B Rq2 Which Ir Models and Enhancement Strategies Have Been A...mentioning

confidence: 99%

A Systematic Mapping Study of Information Retrieval Approaches Applied to Requirements Trace Recovery

Wang¹,

Wang²,

Luo³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Context: Requirements trace recovery (RTR) is always time-consuming, tedious, and fallible. There has been a growing interest in applying information retrieval (IR) to automate the process of recover trace links between requirements artifacts and other software artifacts. Objective: In this review, our objective is to identify the state-of-the-art of how IR has been explored to automate RTR and provide an overview of the research at the intersection of these two fields. Method: A systematic mapping study has been conducted, searching the main scientific databases. The search retrieved 1587 citations and 34 articles are retained as primary studies. Results: The results show the most active authors and publication distribution. It presents four kinds of IR models and 21 enhancement strategies applied to perform RTR. Besides, the lists of 37 experimental datasets and 9 measures, commonly used together to evaluate IR-based RTR approaches, are provided. Conclusions: Vector Space Model (VSM) and Latent Semantic Index (LSI) are the most two studied IR models used in RTR. CoEST becomes the most popular, convenient and stable source of datasets. Precision and Recall are the most common measures used to evaluate the performance of IR methods. Overall, IR-based RTR is becoming an increasingly mature cross research field.Keywords-requirements trace recovery, information retrieval, systematic mapping study retrieved automatically from the databases. 1) Inclusion and exclusion criteria.Once the potentially relevant studies have been obtained, their actual relevance needs to be assessed. We defined the following inclusion and exclusion criteria to select studies from the search results based on the SMS guidelines [5].Inclusion criteria: I1: The time span of the study is 2012.1-2021.12. I2: The research topic of study must be IR-based RTR. I3: The study is not a review paper. I4: The papers are written in English. I5: When two papers with the same technology and topic are provided by the same author, we select the one that is described in greater detail.Exclusion criteria: E1: The time span of the study is not during 2012.1-2021.12.E2: The research topic of study is not IR-based RTR.E3: The study is a review paper. E4: The paper is not written in English. E5: When two papers with the same technology and topic are provided by the same author, we exclude the one that is described less thoroughly.2) Search scope. Time period. We specify the time period of the published studies for this SMS from January 2012 to December 2021, which is when we started this SMS.Electronic databases. Based on the suggestion in [5] and the access authority of our institution, the following databases are selected as the primary study sources: IEEE, Google Scholar, Elsevier, EI Compendex, and Springer.

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Section: B Rq2 Which Ir Models and Enhancement Strategies Have Been A...mentioning

confidence: 99%

A Systematic Mapping Study of Information Retrieval Approaches Applied to Requirements Trace Recovery

Wang¹,

Wang²,

Luo³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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show abstract

“…Therefore, mockups, models, UIs, and other non-textual artifacts are beyond their scope. Similarly, other approaches limit their artifacts to model-based artifacts [12], requirements [9,13], and source code [9,11,13].…”

Section: Related Workmentioning

confidence: 99%

“…In practice, the effort required to maintain, validate, and generate traces between artifacts outweighs traceability benefits [5]. Therefore, some authors propose novel approaches that allow software development teams to create traces between artifacts [5][6][7][8][9][10][11][12][13]. Although such approaches are helpful, some of them require as input existing traceability data sets or existing traces between artifacts [5,6,8,11], hindering their practical applicability by software development teams that do not currently trace their artifacts.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, some authors propose novel approaches that allow software development teams to create traces between artifacts [5][6][7][8][9][10][11][12][13]. Although such approaches are helpful, some of them require as input existing traceability data sets or existing traces between artifacts [5,6,8,11], hindering their practical applicability by software development teams that do not currently trace their artifacts. On the other hand, other approaches limit their scope to specific artifacts [7,9,10,12,13], lacking generality.…”

Section: Introductionmentioning

confidence: 99%

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OntoTrace: A Tool for Supporting Trace Generation in Software Development by Using Ontology-Based Automatic Reasoning

Mosquera,

Ruiz,

Pastor

et al. 2022

Lecture Notes in Business Information Processing

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Traceability in software development has gained interest due to its software maintainability and quality assurance benefits. Artifacts such as code, requirements, mockups, test cases, among others, are feasible trace sources/targets during the software development process. Existing scientific approaches support tasks like identifying untraced artifacts, establishing new traces, and validating existing traces. However, most approaches require input existing traceability data or are restricted to a certain application domain hindering their practical application. This contemporary challenge in information systems engineering calls for novel traceability solutions. In this paper, we present OntoTrace: a tool for supporting traceability tasks in software development projects by using ontology-based automatic reasoning. OntoTrace allows software development teams for inferring traceability-related data such as i) which are the traceable source/target artifacts; ii) which artifacts are not yet traced; and iii) given a specific artifact, which are the possible traces between it and other artifacts. We demonstrate how OntoTrace works in the context of the Swiss startup LogicFlow AG, supporting the traceability between functional/non-functional requirements and user interface test cases. We conclude the paper by reflecting on the experience from applying the approach in practice, and we draw on future challenges and next research endeavors.

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Towards a Recommended Documentation System Using Data Traceability and Machine Learning in a Big Data Environment

Rahmaoui

Souali

Ouzzif

2020

Advances in Intelligent Systems and Computing

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Recovering Traceability Links between Code and Documentation for Enterprise Project Artifacts

Cited by 10 publications

References 12 publications

A Systematic Mapping Study of Information Retrieval Approaches Applied to Requirements Trace Recovery

A Systematic Mapping Study of Information Retrieval Approaches Applied to Requirements Trace Recovery

OntoTrace: A Tool for Supporting Trace Generation in Software Development by Using Ontology-Based Automatic Reasoning

Towards a Recommended Documentation System Using Data Traceability and Machine Learning in a Big Data Environment

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