Traceability recovery in RAD software systems

Penta, Massimiliano Di; Gradara, Sara; Antoniol, Giuliano

doi:10.1109/wpc.2002.1021342

Cited by 21 publications

(22 citation statements)

References 23 publications

(35 reference statements)

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“…Research that categorizes or associates source code with other documents (traceability recovery) is similar to ours in that it gathers terms from source code and then uses learning or statistical approaches to find associated documents [2], [42]. For example, Maletic et al [28], [29] extracted all features available in the source code via Latent Semantic Analysis (LSA) and then used this data to cluster software and to create relationships between the source code and other related software project documents.…”

Section: Classification Clustering Associating and Traceability Rementioning

confidence: 99%

Classifying Software Changes: Clean or Buggy?

Kim

Whitehead²,

Zhang

2008

IIEEE Trans. Software Eng.

548

236

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Abstract-This paper introduces a new technique for predicting latent software bugs, called change classification. Change classification uses a machine learning classifier to determine whether a new software change is more similar to prior buggy changes or clean changes. In this manner, change classification predicts the existence of bugs in software changes. The classifier is trained using features (in the machine learning sense) extracted from the revision history of a software project stored in its software configuration management repository. The trained classifier can classify changes as buggy or clean, with a 78 percent accuracy and a 60 percent buggy change recall on average. Change classification has several desirable qualities: 1) The prediction granularity is small (a change to a single file), 2) predictions do not require semantic information about the source code, 3) the technique works for a broad array of project types and programming languages, and 4) predictions can be made immediately upon the completion of a change. Contributions of this paper include a description of the change classification approach, techniques for extracting features from the source code and change histories, a characterization of the performance of change classification across 12 open source projects, and an evaluation of the predictive power of different groups of features.

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Section: Classification Clustering Associating and Traceability Rementioning

confidence: 99%

Classifying Software Changes: Clean or Buggy?

Kim

Whitehead²,

Zhang

2008

IIEEE Trans. Software Eng.

548

236

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“…The need to provide the software engineer with methods and tools supporting traceability recovery has been widely recognised in the last years. In particular, several researchers have recently applied Information Retrieval (IR) (Baeza-Yates and Ribeiro-Neto 1999;Deerwester et al 1990;Harman 1992) techniques to the problem of recovering traceability links between artefacts of different types (Antoniol et al 2000aCleland-Huang et al 2005;De Lucia et al 2006a, 2007bDi Penta et al 2002;Hayes et al 2003Hayes et al , 2006Marcus and Maletic 2003;Settimi et al 2004).…”

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confidence: 99%

Assessing IR-based traceability recovery tools through controlled experiments

2008

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We report the results of a controlled experiment and a replication performed with different subjects, in which we assessed the usefulness of an Information Retrieval-based traceability recovery tool during the traceability link identification process. The main result achieved in the two experiments is that the use of a traceability recovery tool significantly reduces the time spent by the software engineer with respect to manual tracing. Replication with different subjects allowed us to investigate if subjects' experience and ability play any role in the traceability link identification process. In particular, we made some observations concerning the retrieval accuracy achieved by the software engineers with and without the tool support and with different levels of experience and ability.

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“…Research related to traceability links between requirements, documentation and source code has been presented in Antoniol et al [2,3] and Penta et al [32]. The core viewpoint is that information retrieval techniques can provide ways to semi-automatically recover traceability links between documentation of a system and its source code.…”

Section: Review Of Related Workmentioning

confidence: 99%

Exploring a Bayesian and linear approach to requirements traceability

Omoronyia

Sindre

Stålhane

2011

Information and Software Technology

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For large software projects it is important to have some traceability between artefacts from different phases (e.g., requirements, designs, code), and between artefacts and the involved developers. This is especially critical during maintenance, when people working on the software may be different from the original developers and therefore have a harder struggle to understand the artefacts and the consequences of changes. However, if the capturing of traceability information during the project is felt as laborious to the original developers, they will often be sloppy in registering the relevant traceability links so that the information is incomplete. This makes automated toolbased collection of traceability links a tempting alternative, but this has the opposite challenge of generating too many potential trace relationships, not all of which are equally relevant. A key issue is therefore how to rank such auto-generated trace relationships. This paper presents two approaches for such a ranking: a Bayesian technique and a linear inference technique. Both techniques depend on the interaction event trails left behind by collaborating developers while working within a development tool. The advantage of our approach is that it can be used to provide traceability insights that are contextual and would have been much more difficult to capture manually. The outcome of a preliminary study suggest the advantage of the linear approach, we also explore the challenges and potentials of the two techniques. Finally we present some key lessons learnt during this research.

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Traceability recovery in RAD software systems

Abstract: Abstract

Cited by 21 publications

References 23 publications

Classifying Software Changes: Clean or Buggy?

Classifying Software Changes: Clean or Buggy?

Assessing IR-based traceability recovery tools through controlled experiments

Exploring a Bayesian and linear approach to requirements traceability

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