An ontology for specifying and tracing requirements engineering artifacts and Test Artifacts

Nowadays, modern software development processes follow an iterative approach, which makes possible to start the testing of a system at early stages. This approach requires recording the requirements artifacts that specify the functionality or characteristics required by the system, and the test cases that are derived from each requirement artifact. Frequently, software development organizations employ supporting tools to create and maintain these artifacts. There exist numerous tools for supporting requirements specification activities, as well as the definition and execution of test cases. These separate tools have their own databases and metamodels. The lack of integration between these tools leads to difficulties in tracing related artifacts and obtaining useful knowledge to manage the developing process. It is necessary to understand without ambiguities the concepts used by the different tools to allow them to interoperate. This paper proposes an ontology that defines and integrates the concepts included by the metamodels of different Requirements Engineering and Testing Management supporting tools. The formalization of these concepts and their relationships in an ontology language prevents ambiguity of the concepts and permit to the tools involved to interoperate with each other, to achieve semantic consistency and the tracing of artifacts. The proposed ontology used in conjunction with a reasoner provides capabilities to infer traces that are not explicit, which makes it possible to easily maintain artifacts and associations between them. The approach facilitates backward tracing from test cases to use cases and functional requirements artifacts, obtain knowledge about the causes of a defect or a poor specification, and enable impact analysis.Fil: Roldán, María Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Vegetti, Maria Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Gonnet, Silvio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Marciszack, Marcelo Martín. Universidad Tecnológica Nacional. Facultad Regional Córdoba; ArgentinaFil: Leone, Horacio Pascual. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Semantic recovery of traceability links between system artifacts

This paper introduces a mechanism to recover traceability links between the requirements and logical models in the context of critical systems development. Currently, lifecycle processes are covered by a good number of tools that are used to generate different types of artifacts. One of the cornerstone capabilities in the development of critical systems lies in the possibility of automatically recovery traceability links between system artifacts generated in different lifecycle stages. To do so, it is necessary to establish to what extent two or more of these work products are similar, dependent or should be explicitly linked together. However, the different types of artifacts and their internal representation depict a major challenge to unify how system artifacts are represented and, then, linked together. That is why, in this work, a concept-based representation is introduced to provide a semantic and unified description of any system artifact. Furthermore, a traceability function is defined and implemented to exploit this new semantic representation and to support the recovery of traceability links between different types of system artifacts. In order to evaluate the traceability function, a case study in the railway domain is conducted to compare the precision and recall of recovery traceability links between text-based requirements and logical model elements. As the main outcome of this work, the use of a concept-based paradigm to represent that system artifacts are demonstrated as a building block to automatically recover traceability links within the development lifecycle of critical systems.The research leading to these results has received funding from the H2020 ECSEL Joint Undertaking (JU) under Grant Agreement No. 826452 \Arrowhead Tools for Engineering of Digitalisation Solutions" and from speci¯c national programs and/or funding authorities

Can Clustering Improve Requirements Traceability? A Tracelab-enabled Study

Software permeates every aspect of our modern lives. In many applications, such in the software for airplane flight controls, or nuclear power control systems software failures can have catastrophic consequences. As we place so much trust in software, how can we know if it is trustworthy? Through software assurance, we can attempt to quantify just that. Building complex, high assurance software is no simple task. The difficult information landscape of a software engineering project can make verification and validation, the process by which the assurance of a software is assessed, very difficult. In order to manage the inevitable information overload of complex software projects, we need software traceability, the ability to describe and follow the life of a requirement, in both forwards and backwards direction. The Center of Excellence for Software Traceability (CoEST) has created a compelling research agenda with the goal of ubiquitous traceability by 2035. As part of this goal, they have developed TraceLab, a visual experimental workbench built to support design, implementation, and execution of traceability experiments. Through our collaboration with CoEST, we have made several contributions to TraceLab and its community. This work contributes to the goals of the traceability research community. The three key contributions are (a) a machine learning component package for TraceLab featuring six (6) classifier algorithms, five (5) clustering algorithms, and a total of over 40 components for creating TraceLab experiments, built upon the WEKA machine learning package, as well as implementing methods outside of WEKA; (b) the design for an automated tracing system that uses clustering to decompose the task of tracing into many smaller tracing subproblems; and (c) an implementation of several key components of this tracing system using TraceLab and its experimental evaluation

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

In vivo evaluation of large-scale IR-based traceability recovery

Modern large-scale software development is a complex undertaking and coordinating various processes is crucial to achieve efficiency. The alignment between requirements and test activities is one important aspect. Production and maintenance of software result in an ever-increasing amount of information. To be able to work efficiently under such circumstances, navigation in all available data needs support. Maintaining traceability links between software artifacts is one approach to structure the information space and support this challenge. Many researchers have proposed traceability recovery by applying information retrieval (IR) methods, utilizing the fact that artifacts often have textual content in natural language. Case studies have shown promising results, but no large-scale in vivo evaluations have been made. Currently, there is a trend among our industrial partners to move to a specific new software engineering tool. Their aim is to collect different pieces of information in one system. Our ambition is to develop an IR-based traceability recovery plug-in to this tool. From this position, right in the middle of a real industrial setting, many interesting observations could be made. This would allow a unique evaluation of the usefulness of the IR-based approach

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

Toward Visualization and Analysis of Traceability Relationships in Distributed and Offshore Software Development Projects

Offshore software development projects provoke new issues to the collaborative endeavor of software development due to their global distribution and involvement of various people, processes, and tools. These problems relate to the geographical distance and the associated time-zone differences; cultural, organizational, and process issues; as well as language problems. However, existing tool support is neither adequate nor grounded in empirical observations. This paper presents two empirical studies of global software development teams and their usage of tools. The results are then used to motivate and inform the construction of more useful software development tools. The focus is on issues that are tool-related but have not yet been solved by existing tools. The two software tools presented as solutions, Ariadne and TraVis, explicitly address yet unresolved issues in global software development and also integrate with prevalent other solutions

Visual language representation for use case evolution and traceability

The primary goal of this research is to assist non-technical stakeholders involved in requirements engineering with a comprehensible method for managing changing requirements within a specific domain. An important part of managing evolving requirements over time is to maintain a temporal ordering of the changes and to support traceability of the modifications. This research defines a semi-formal syntactical and semantic definition of such a method using a visual language, RE/TRAC (Requirements Evolution with Traceability), and a supporting formal semantic notation RE/TRAC-SEM. RE/TRAC-SEM is an ontological specification employing a combination of models, including verbal definitions, set theory and a string language specification RE/TRAC-CF. The language RE/TRAC-CF enables the separation of the syntactical description of the visual language from the semantic meaning of the model, permitting varying target representations and taking advantage of existing efficient parsing algorithms for context-free grammars. As an application of the RE/TRAC representation, this research depicts the hierarchical step-wise refinement of UML use case diagrams to demonstrate evolving system requirements. In the current arena of software development, where systems are described using platform independent models (PIMs) which emphasize the front-end design process, requirements and design documents, including the use cases, have become the primary artifacts of the system. Therefore the management of requirements’ evolution has become even more critical in the creation and maintenance of systems

Traceability for the maintenance of secure software

Traceability links among different software engineering artifacts make explicit how a software system was implemented to accommodate its requirements. For secure and dependable software system development, one must ensure the linked entities are truly traceable to each other and the links are updated to reflect true traceability among changed entities. However, traditional traceability relationships link recovery techniques are not accurate enough. To address this problem, we propose a traceability technique based on refactoring, which is then continuously integrated with other software maintenance activities. Applying our traceability technique to the proven SSL protocol design, we found a significant vulnerability bug in its open-source implementation. The results also demonstrate the level of accuracy and change resilience of our technique that enable reuse of the traceability-related analysis on different implementations