Combining textual and structural analysis of software artifacts for traceability link recovery

McMillan, Collin; Poshyvanyk, Denys; Revelle, Meghan

doi:10.1109/tefse.2009.5069582

Cited by 74 publications

(46 citation statements)

References 24 publications

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“…There are reports on several trace automation approaches, such as Egyed's scenario-driven approach to traceability [6], Jackson's key-phrase-based traceability scheme [12]. Further, there are the heterogeneous traceability approach Cleland-Huang et al [4], and approaches by Pinheiro et al [20], Leuser [14], and McMillan et al [16]. These approaches use different techniques to identify requirements interdependencies.…”

Section: Requirement Conflicts Detection and Requirements Tracingmentioning

confidence: 99%

Requirements Management with Semantic Technology: An Empirical Study on Automated Requirements Categorization and Conflict Analysis

Moser

Winkler

Heindl

et al. 2011

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract.Requirements managers aim at keeping the set of requirements consistent and up to date throughout the project by conducting the following tasks: requirements categorization, requirements conflict analysis, and requirements tracing. However, the manual conduct of these tasks takes significant effort and is error-prone. In this paper we propose to use semantic technology as foundation for automating the requirements management tasks and introduce the ontology-based reporting approach OntRep. We evaluate the effectiveness and effort the OntRep approach based on a real-world industrial empirical study with professional Austrian IT project managers. Major results were that OntRep provides reasonable capabilities for the automated categorization of requirements, was when compared to a manual approach considerably more effective to identify conflicts, and produced less false positives with similar effort.

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Section: Requirement Conflicts Detection and Requirements Tracingmentioning

confidence: 99%

Requirements Management with Semantic Technology: An Empirical Study on Automated Requirements Categorization and Conflict Analysis

Moser

Winkler

Heindl

et al. 2011

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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“…The current work continues today on improving existing techniques for feature location, impact analysis, traceability link recovery, software reuse, mining developer expertise, and already resulted in several refereed conference publications [10,21,22,24,32].…”

Section: Publications and Current Workmentioning

confidence: 99%

Using information retrieval to support software maintenance tasks

Poshyvanyk

2009

2009 IEEE International Conference on Software Maintenance

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“…Latent Semantic Indexing identifies relationships between terms and concepts in unstructured text and has been successfully applied to a number of software engineering tasks such as feature location [45,160,165,173], traceability link recovery [3,57,100,108,137,148,220], software measurement [139,164], and detecting code clones [136,212]. In LSI, Identifiers and comments extracted from source code comprise a vocabulary set.…”

Section: Unstructured Informationmentioning

confidence: 99%

Supporting Feature-Level Software Maintenance

Revelle

2009

2009 16th Working Conference on Reverse Engineering

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Software maintenance is the process of modifying a software system to fix defects, improve performance, add new functionality, or adapt the system to a new environment. A maintenance task is often initiated by a bug report or a request for new functionality. Bug reports typically describe problems with incorrect behaviors or functionalities. These behaviors or functionalities are known as features. Even in very well-designed systems, the source code that implements features is often not completely modularized. The delocalized nature of features makes maintaining them challenging. Since maintenance tasks are expressed in terms of features, the goal of this dissertation is to support software maintenance at the feature-level. We focus on two tasks in particular: feature location and impact analysis via feature coupling.Feature location is the process of identifying the source code that implements a feature, and it is an essential first step to any maintenance task. There are many existing techniques for feature location that incorporate various types of analyses such as static, dynamic, and textual. In this dissertation, we recognize the advantages of leveraging several types of analyses and introduce a new approach to feature location based on combining dynamic analysis, textual analysis, and web mining algorithms applied to software. The use of web mining for feature location is a novel contribution, and we show that our new techniques based on web mining are significantly more effective than the current state of the art.After using feature location to identify a feature's source code, maintenance can be completed on that feature. Impact analysis should then be performed to revalidate the system and determine which other features may have been affected by the modifications. We define three feature coupling metrics that capture the relationship between features based on structural information, textual information, and their combination. Our novel feature coupling metrics can be used for impact analysis to quantify the strength of coupling between pairs of features. We performed three empirical studies on open-source software systems to asses the feature coupling metrics and established three major results. First,

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Combining textual and structural analysis of software artifacts for traceability link recovery

Abstract: Abstract

Cited by 74 publications

References 24 publications

Requirements Management with Semantic Technology: An Empirical Study on Automated Requirements Categorization and Conflict Analysis

Requirements Management with Semantic Technology: An Empirical Study on Automated Requirements Categorization and Conflict Analysis

Using information retrieval to support software maintenance tasks

Supporting Feature-Level Software Maintenance

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