Using code ownership to improve IR-based Traceability Link Recovery

Diaz, Diana; Bavota, Gabriele; Marcus, Andrian; Oliveto, Rocco; Takahashi, S.; Lucia, Andrea De

doi:10.1109/icpc.2013.6613840

Cited by 29 publications

(12 citation statements)

References 34 publications

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“…Requirements‐to‐code traceability and the activity of retrieving them, often referred to as feature location , also received considerable attention by the scientific community. Information retrieval (IR), perhaps to date the most widely applied and studied technology in the traceability community, identifies traces based on naming similarities between source code and other software artifacts, like requirements . However, an important issue hindering the performance of IR techniques when applied to the recovery of traceability is the problem of vocabulary mismatch between source and target artifacts (like requirements and code).…”

Section: Related Workmentioning

confidence: 99%

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Can method data dependencies support the assessment of traceability between requirements and source code?

Kuang

Mäder

et al. 2015

J. Softw. Evol. and Proc.

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Requirements traceability benefits many software engineering activities, such as change impact analysis and risk assessment. However, these activities require complete and correct traceability links which is not trivial, making traceability assessment an important field of study. In recent years, requirements traceability research has focused on using call dependencies within source code to understand how code properties contribute to the implementation of a requirement and to assess whether traceability links are correct and complete. These approaches largely ignore the role of existing data dependencies within the source code. That is, methods may never call each other, but may still depend upon another by sharing data. We identified five research questions and validated them on five software systems, covering 4 to 72 KLOC. We found that data dependencies are as relevant as call dependencies for assessing requirements traceability. Even more interesting, our analyses show that data dependencies complement call dependencies in the assessment. These findings have strong implications on code understanding, including trace capture, maintenance, and validation techniques.

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Section: Related Workmentioning

confidence: 99%

“…Diaz et al . introduced additional ‘author contexts’ from code to improve IR‐based traceability links recovery. They find all code snippets that are commented by each author of the code based on Javadoc‐style comments and generate so‐called ‘author contexts’.…”

Section: Related Workmentioning

confidence: 99%

Can method data dependencies support the assessment of traceability between requirements and source code?

Kuang

Mäder

et al. 2015

J. Softw. Evol. and Proc.

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show abstract

“…Diaz et al [14] capture relationships between source code artifacts to improve the recovery of traceability links between documentation and source code. They extract the author of each source code component and, for each author, identify the \context" she/he worked on.…”

Section: Source Code-to-documentation Traceability Linksmentioning

confidence: 99%

Automatic Documentation of [Mined] Feature Implementations from Source Code Elements and Use-Case Diagrams with the REVPLINE Approach

Al-Msie’deen

Huchard

Seriai

et al. 2014

Int. J. Soft. Eng. Knowl. Eng.

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International audienceCompanies often develop a set of software variants that share some features and differ in other ones to meet specific requirements. To exploit the existing software variants as a Software Product Line (SPL), a Feature Model of this SPL must be built as a first step. To do so, it is necessary to define and document the optional and mandatory features that compose the variants. In our previous work, we mined a set of feature implementations as identified sets of source code elements. In this paper, we propose a complementary approach, which aims to document the mined feature implementations by giving them names and descriptions, based on the source code elements that form feature implementations and the use-case diagrams that specify software variants. The novelty of our approach is its use of commonality and variability across software variants, at feature implementation and use-case levels, to run Information Retrieval methods in an efficient way. Experiments on several real case studies (Mobile media and ArgoUML-SPL) validate our approach and show promising results

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“…Ali et al [22] proposed an approach to establish and maintain traceability links between source codes and software requirements to improve the precision and recall of information retrieval (IR) techniques by discarding/re-ranking the reported links from IR based on the outcome links that are generated from software repositories. Diaz, Diana, et al [23] proposed an approach that purifies the noise from the candidate links list generated by IR technique. This approach leverages the code ownership to improve the traceability link recovery.…”

Section: Information Retrieval Techniquesmentioning

confidence: 99%

“…If the categories are matched at this level (i.e. current level) then we increment the similarity score by the value of I (line [22][23][24][25][26][27][28][29]. Consider for example that the subject Florida is in artifact 1 and the artifact 2, has the subject Arizona.…”

Section: Inference Modulementioning

confidence: 99%

Autonomous Decentralized Semantic Based Traceability Link Recovery Framework

Mahmood

Alobaidi

Takahashi³

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThe automation of traceability links or traceability matrices is important to many software development paradigms. In turn, the efficiency and effectiveness of the recovery of traceability links in the distributed software development is becoming increasingly vital due to complexity of project developments, as this include continuous change in requirements, geographically dispersed project teams, and the complexity of managing the elements of a project -time, money, scope, and people. Therefore, the traceability links among the requirements artifacts, which fulfill business objectives, is also critical to reduce the risk and ensures project's success. This paper proposes Autonomous Decentralized Semantic based Traceability Link Recovery (AD-STLR) architecture. According to best of our knowledge this is the first architectural approach that uses an autonomous decentralized concept, DBpedia knowledge-base, Babelnet 2.5 multilingual dictionary and semantic network, for finding similarity among different project artifacts and the automation of traceability links recovery.

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Using code ownership to improve IR-based Traceability Link Recovery

Cited by 29 publications

References 34 publications

Can method data dependencies support the assessment of traceability between requirements and source code?

Can method data dependencies support the assessment of traceability between requirements and source code?

Automatic Documentation of [Mined] Feature Implementations from Source Code Elements and Use-Case Diagrams with the REVPLINE Approach

Autonomous Decentralized Semantic Based Traceability Link Recovery Framework

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