Preventing Defects: The Impact of Requirements Traceability Completeness on Software Quality

Rempel, Patrick; Mader, Parick

doi:10.1109/tse.2016.2622264

Cited by 70 publications

(30 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We denote R as a set of requirement specifications that explicitly describe the function and nonfunction constraints that should be implemented in the software system, D as a set of the artifacts in the design model that contain explicit instructions on how to build a software system in order to satisfy R, and S as a set of source codes that implement D in order to build the software system. In addition, the software development life cycle spans two different stages: the initial development stage and the evolution and refinement stage [36]. We denote CR as a set of requirement change specifications that describe how a software system is supposed to be changed to meet newly emerged, shifted, or misunderstood customers' expectations.…”

Section: Traceability Scenariosmentioning

confidence: 99%

An Approach to Generate the Traceability Between Restricted Natural Language Requirements and AADL Models

Wang¹,

Yang²,

Huang³

et al. 2020

IEEE Trans. Rel.

View full text Add to dashboard Cite

Requirements traceability is broadly recognized as a critical element of any rigorous software development process, especially for building safety-critical software (SCS) systems. Modeldriven development (MDD) is increasingly used to develop SCS in many domains, such as automotive and aerospace. MDD provides new opportunities for establishing traceability links through modeling and model transformations. Architecture Analysis and Design Language (AADL) is a standardized architecture description language for embedded systems, which is widely used in avionics and aerospace industries to model safety-critical applications. However, there is a big challenge to automatically establish the traceability links between requirements and AADL models in the context of MDD, because requirements are mostly written as free natural language texts, which are often ambiguous and difficult to be processed automatically. To bridge the gap between natural language requirements (NLRs) and AADL models, we propose an approach to generate the traceability links between NLRs and AADL models. First, we propose a requirement modeling method based on the restricted natural language, which is named as RM-RNL. The RM-RNL can eliminate the ambiguity of NLRs and barely change engineers' habits of requirement specification. Second, we present a method to automatically generate the initial AADL models from the RM-RNLs and to automatically establish traceability links between the elements of the RM-RNL and the generated AADL models. Third, we refine the initial AADL models through patterns to achieve the change of requirements and traceability links. Finally, we demonstrate the effectiveness of our approach with industrial case studies and evaluation experiments.

show abstract

Section: Traceability Scenariosmentioning

confidence: 99%

An Approach to Generate the Traceability Between Restricted Natural Language Requirements and AADL Models

Wang¹,

Yang²,

Huang³

et al. 2020

IEEE Trans. Rel.

View full text Add to dashboard Cite

show abstract

“…Instead we direct the interested reader to a 2010 review of these algorithms [30] for a detailed discussion. Note that for our analysis we use the standard Weka implementation of each algorithm without any special parameter tuning 3 . This allows us to compare classification algorithms in their default state, realizing that additional tuning can further improve performance.…”

Section: Classification Algorithmsmentioning

confidence: 99%

“…The resulting software traceability naturally supports other tasks such as concept location, impact analysis, program comprehension, verifying test coverage, ensuring that system and regulatory requirements are met, etc. and has been proven to be useful in practice [1], [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Traceability Maintenance via Machine Learning Classification

Mills

Escobar-Avila

Haiduc

2018

2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

Previous studies have shown that software traceability, the ability to link together related artifacts from different sources within a project (e.g., source code, use cases, documentation, etc.), improves project outcomes by assisting developers and other stakeholders with common tasks such as impact analysis, concept location, etc. Establishing traceability links in a software system is an important and costly task, but only half the struggle. As the project undergoes maintenance and evolution, new artifacts are added and existing ones are changed, resulting in outdated traceability information. Therefore, specific steps need to be taken to make sure that traceability links are maintained in tandem with the rest of the project.In this paper we address this problem and propose a novel approach called TRAIL for maintaining traceability information in a system. The novelty of TRAIL stands in the fact that it leverages previously captured knowledge about project traceability to train a machine learning classifier which can then be used to derive new traceability links and update existing ones. We evaluated TRAIL on 11 commonly used traceability datasets from six software systems and compared it to seven popular Information Retrieval (IR) techniques including the most common approaches used in previous work. The results indicate that TRAIL outperforms all IR approaches in terms of precision, recall, and F-score.

show abstract

“…Closed Created: 30/Dec/11 09:59 Resolution: Fixed Resolved: 03/Jan/12 02:29 Figure 1: Example of an improvement in Jira issue tracker defect prevention [53], change impact analysis, coverage analysis, and even provides enhanced support for building recommendation systems to identify appropriate developers for fixing bugs [2]. We train and evaluate our approach on six open-source projects in order to address three key research questions: RQ1: Is the link classifier able to accurately reconstruct issue tags during the commit process?…”

Section: Statusmentioning

confidence: 99%

Traceability in the wild

Rath

Rendall

Guo

et al. 2018

Proceedings of the 40th International Conference on Software Engineering

View full text Add to dashboard Cite

Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%.

show abstract

Preventing Defects: The Impact of Requirements Traceability Completeness on Software Quality

Cited by 70 publications

References 76 publications

An Approach to Generate the Traceability Between Restricted Natural Language Requirements and AADL Models

An Approach to Generate the Traceability Between Restricted Natural Language Requirements and AADL Models

Automatic Traceability Maintenance via Machine Learning Classification

Traceability in the wild

Contact Info

Product

Resources

About