2,555 research outputs found
Automating Requirements Traceability: Two Decades of Learning from KDD
This paper summarizes our experience with using Knowledge Discovery in Data
(KDD) methodology for automated requirements tracing, and discusses our
insights.Comment: The work of the second author has been supported in part by NSF
grants CCF-1511117 and CICI 1642134; 4 pages; in Proceedings of IEEE
Requirements Engineering 201
Relating Developers’ Concepts and Artefact Vocabulary in a Financial Software Module
Developers working on unfamiliar systems are challenged to accurately identify where and how high-level concepts are implemented in the source code. Without additional help, concept location can become a tedious, time-consuming and error-prone task. In this paper we study an industrial financial application for which we had access to the user guide, the source code, and some change requests. We compared the relative importance of the domain concepts, as understood by developers, in the user manual and in the source code. We also searched the code for the concepts occurring in change requests, to see if they could point developers to code to be modified. We varied the searches (using exact and stem matching, discarding stop-words, etc.) and present the precision and recall. We discuss the implication of our results for maintenance
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Grand Challenges of Traceability: The Next Ten Years
In 2007, the software and systems traceability community met at the first
Natural Bridge symposium on the Grand Challenges of Traceability to establish
and address research goals for achieving effective, trustworthy, and ubiquitous
traceability. Ten years later, in 2017, the community came together to evaluate
a decade of progress towards achieving these goals. These proceedings document
some of that progress. They include a series of short position papers,
representing current work in the community organized across four process axes
of traceability practice. The sessions covered topics from Trace Strategizing,
Trace Link Creation and Evolution, Trace Link Usage, real-world applications of
Traceability, and Traceability Datasets and benchmarks. Two breakout groups
focused on the importance of creating and sharing traceability datasets within
the research community, and discussed challenges related to the adoption of
tracing techniques in industrial practice. Members of the research community
are engaged in many active, ongoing, and impactful research projects. Our hope
is that ten years from now we will be able to look back at a productive decade
of research and claim that we have achieved the overarching Grand Challenge of
Traceability, which seeks for traceability to be always present, built into the
engineering process, and for it to have "effectively disappeared without a
trace". We hope that others will see the potential that traceability has for
empowering software and systems engineers to develop higher-quality products at
increasing levels of complexity and scale, and that they will join the active
community of Software and Systems traceability researchers as we move forward
into the next decade of research
Recovering Trace Links Between Software Documentation And Code
Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions.
Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using componentbased architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code.
Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines.
Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches
Recovering Trace Links Between Software Documentation And Code
Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions.
Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using componentbased architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code.
Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines.
Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches
Requirements Traceability: Recovering and Visualizing Traceability Links Between Requirements and Source Code of Object-oriented Software Systems
Requirements traceability is an important activity to reach an effective
requirements management method in the requirements engineering.
Requirement-to-Code Traceability Links (RtC-TLs) shape the relations between
requirement and source code artifacts. RtC-TLs can assist engineers to know
which parts of software code implement a specific requirement. In addition,
these links can assist engineers to keep a correct mental model of software,
and decreasing the risk of code quality degradation when requirements change
with time mainly in large sized and complex software. However, manually
recovering and preserving of these TLs puts an additional burden on engineers
and is error-prone, tedious, and costly task. This paper introduces YamenTrace,
an automatic approach and implementation to recover and visualize RtC-TLs in
Object-Oriented software based on Latent Semantic Indexing (LSI) and Formal
Concept Analysis (FCA). The originality of YamenTrace is that it exploits all
code identifier names, comments, and relations in TLs recovery process.
YamenTrace uses LSI to find textual similarity across software code and
requirements. While FCA employs to cluster similar code and requirements
together. Furthermore, YamenTrace gives a visualization of recovered TLs. To
validate YamenTrace, it applied on three case studies. The findings of this
evaluation prove the importance and performance of YamenTrace proposal as most
of RtC-TLs were correctly recovered and visualized.Comment: 17 pages, 14 figure
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