REquirements TRacing On target (RETRO): improving software maintenance through traceability recovery

Hayes, Jane Huffman; Dekhtyar, Alex; Sundaram, Senthil Karthikeyan; Holbrook, E. Ashlee; Vadlamudi, Sravanthi; April, Alain

doi:10.1007/s11334-007-0024-1

Cited by 87 publications

(69 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Typically, when we consider a precision-recall curve, we observe: high recall values and low precision; high precision and low recall; and values in between these two extremes [12], [13], [2]. High precision and low recall implies that we accurately retrieved a small fraction of the required documents, but not most of them.…”

Section: Observationsmentioning

confidence: 99%

“…Most have applied the vector space model to generate traceability between various artifact pairs (such as source code modules and manual pages, functional requirements and source code) [11,13]. In general, high values of recall are achieved (90 -100%) with low precision (2 -12%) [11,2,3]. As a result, work has focused on improving precision.…”

Section: A Candidate Link Generation/text Analysismentioning

confidence: 99%

“…Requirements tracing ensures that requirements are properly addressed in the resulting software artifacts, i.e., use cases, test cases, etc. In recent years, interest in this area has intensified as demonstrated by the amount of work on automating the process of requirement tracing [2][3][4] [5]. It should be noted that even the automated traceability techniques require some human analyst involvement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of reinforcement learning to requirements engineering: requirements tracing

Sultanov

Hayes

2013

2013 21st IEEE International Requirements Engineering Conference (RE)

Self Cite

View full text Add to dashboard Cite

Abstract-We posit that machine learning can be applied to effectively address requirements engineering problems. S pecifically, we present a requirements traceability method based on the machine learning technique Reinforcement Learning (RL). The RL method demonstrates a rather targeted generation of candidate links between textual requirements artifacts (high level requirements traced to low level requirements, for example). The technique has been validated using two real -world datasets from two problem domains.Our technique demonstrated statistically significant better results than the Information Retrieval technique.

show abstract

Section: Observationsmentioning

confidence: 99%

Section: A Candidate Link Generation/text Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of reinforcement learning to requirements engineering: requirements tracing

Sultanov

Hayes

2013

2013 21st IEEE International Requirements Engineering Conference (RE)

Self Cite

View full text Add to dashboard Cite

show abstract

“…RETRO: RETRO (REquirements TRacing On target) [10] is an automated tool for generating traceability links among textual artefacts. RETRO implements various IR-based techniques for link generation.…”

Section: A Toolsmentioning

confidence: 99%

Identifying outdated requirements based on source code changes

Charrada

Koziolek

Glinz

2012

2012 20th IEEE International Requirements Engineering Conference (RE)

View full text Add to dashboard Cite

Keeping requirements specifications up-to-date when systems evolve is a manual and expensive task. Software engineers have to go through the whole requirements document and look for the requirements that are affected by a change. Consequently, engineers usually apply changes to the implementation directly and leave requirements unchanged. In this paper, we propose an approach for automatically detecting outdated requirements based on changes in the code. Our approach first identifies the changes in the code that are likely to affect requirements. Then it extracts a set of keywords describing the changes. These keywords are traced to the requirements specification, using an existing automated traceability tool, to identify affected requirements. Automatically identifying outdated requirements reduces the effort and time needed for the maintenance of requirements specifications significantly and thus helps preserve the knowledge contained in them. We evaluated our approach in a case study where we analyzed two consecutive source code versions and were able to detect 12 requirements-related changes out of 14 with a precision of 79%. Then we traced a set of keywords we extracted from these changes to the requirements specification. In comparison to simply tracing changed classes to requirements, we got better results in most cases. Identifying Outdated Requirements Based on Source Code ChangesEya Ben Charrada, Anne Koziolek, Martin Glinz Departement of Informatics, University of Zurich, Switzerland {charrada, koziolek, glinz}@ifi.uzh.chAbstract-Keeping requirements specifications up-to-date when systems evolve is a manual and expensive task. Software engineers have to go through the whole requirements document and look for the requirements that are affected by a change. Consequently, engineers usually apply changes to the implementation directly and leave requirements unchanged.In this paper, we propose an approach for automatically detecting outdated requirements based on changes in the code. Our approach first identifies the changes in the code that are likely to affect requirements. Then it extracts a set of keywords describing the changes. These keywords are traced to the requirements specification, using an existing automated traceability tool, to identify affected requirements.Automatically identifying outdated requirements reduces the effort and time needed for the maintenance of requirements specifications significantly and thus helps preserve the knowledge contained in them.We evaluated our approach in a case study where we analyzed two consecutive source code versions and were able to detect 12 requirements-related changes out of 14 with a precision of 79%. Then we traced a set of keywords we extracted from these changes to the requirements specification. In comparison to simply tracing changed classes to requirements, we got better results in most cases.

show abstract

“…In our previous work [15,16,20], we studied approaches to automating the requirements tracing process by using Information Retrieval (IR) methods to propose candidate links between a pair of project artifacts as well as ways to incorporate automated traceability into the IV&V process [14,29].…”

Section: Introductionmentioning

confidence: 99%

Assessing traceability of software engineering artifacts

et al. 2010

Self Cite

View full text Add to dashboard Cite

The generation of traceability links or traceability matrices is vital to many software engineering activities. It is also person-power intensive, time-consuming, error-prone, and lacks tool support. The activities that require traceability information include, but are not limited to, risk analysis, impact analysis, criticality assessment, test coverage analysis, and verification and validation of software systems. Information Retrieval (IR) techniques have been shown to assist with the automated generation of traceability links by reducing the time it takes to generate the traceability mapping. Researchers have applied techniques such as Latent Semantic Indexing (LSI), vector space retrieval, and probabilistic IR and have enjoyed some success. This paper concentrates on examining issues not previously widely studied in the context of traceability: the importance of the vocabulary base used for tracing and the evaluation and assessment of traceability mappings and methods using secondary measures. We examine these areas and perform empirical studies to understand the importance of each to the traceability of software engineering artifacts.

show abstract

REquirements TRacing On target (RETRO): improving software maintenance through traceability recovery

Cited by 87 publications

References 22 publications

Application of reinforcement learning to requirements engineering: requirements tracing

Application of reinforcement learning to requirements engineering: requirements tracing

Identifying outdated requirements based on source code changes

Assessing traceability of software engineering artifacts

Contact Info

Product

Resources

About