Service Oriented Architecture for a Software Traceability System

In order to improve software development and keep up with the fast pace of business, standards and methodologies for determining and endorsing effective software development processes have been introduced and put into effect on software projects. Accordingly, many tools that interpret these standards and methodologies have been developed and employed. Although there is active development and research in the area of requirements traceability, the desired level of acceptance has not been achieved, and the most widely reported reason for this in the industry, is that of: ‘poor and immature integration technology’. This has resulted in existing tools often suffering problems due to poor integration and inflexibility with other technologies, which undermines the usefulness, usability and longevity of the Requirements Traceability provided by these tools. The panacea, at least in the confines of this project, is to employ a new technology: ’Web Services’ as the underlying framework, to address these problems. The motivation for employing the web services architecture for this project is to allow personalized customization of a traceability solution, hence providing a ubiquitous software development process that incorporates standards as well as software engineering industry best practices

A preliminary study of software trace ability reference model using feature modeling

Traceability, a key aspect of any engineering discipline, enables engineers to understand the relations and dependencies among various artifacts in a system. It is a well-known fact that even in organizations and projects with mature software development processes, software artifacts created as part of these processes end up to be disconnected from each other. From a software engineer’s perspective, it therefore becomes essential to establish and maintain the semantic connections among these artifacts. The missing traceability among software artifacts becomes a major challenge for many software engineering activities. As a result, during the comprehension of existing software systems, software engineers have to spend a large amount of effort on synthesizing and integrating information from various sources to establish links among these artifacts. Existing research in software traceability focuses on reducing the cost associated with this manual effort by developing automatic assistance in establishing and maintaining traceability links among software artifacts. This research is based on the premise that a more effective and unified solution to manage traceability semantic link information can be achieved by considering a feature model as the traceability reference model. The aim of this research is to propose a software traceability reference model that can store traceability links information using the concept of feature modeling. It identifies the traces of software components of various software artifacts such as design and requirements and stores them in hierarchical form

Clustering and its Application in Requirements Engineering

Large scale software systems challenge almost every activity in the software development life-cycle, including tasks related to eliciting, analyzing, and specifying requirements. Fortunately many of these complexities can be addressed through clustering the requirements in order to create abstractions that are meaningful to human stakeholders. For example, the requirements elicitation process can be supported through dynamically clustering incoming stakeholders’ requests into themes. Cross-cutting concerns, which have a significant impact on the architectural design, can be identified through the use of fuzzy clustering techniques and metrics designed to detect when a theme cross-cuts the dominant decomposition of the system. Finally, traceability techniques, required in critical software projects by many regulatory bodies, can be automated and enhanced by the use of cluster-based information retrieval methods. Unfortunately, despite a significant body of work describing document clustering techniques, there is almost no prior work which directly addresses the challenges, constraints, and nuances of requirements clustering. As a result, the effectiveness of software engineering tools and processes that depend on requirements clustering is severely limited. This report directly addresses the problem of clustering requirements through surveying standard clustering techniques and discussing their application to the requirements clustering process

A Multi-Model View of Process Modelling

International audienceSituatedness of development processes is a key issue in both the software engineering and the method engineering communities, as there is a strong felt need for process prescriptions to be adapted to the situation at hand. The assumption of the process modelling approach presented in this paper is that process prescriptions shall be selected according to the actual situation at hand i.e. dynamically in the course of the process. The paper focuses on a multi-model view of process modelling which supports this dynamicity. The approach builds on the notion of a labelled graph of intentions and strategies called a map as well as its associated guidelines. The map is a navigational structure which supports the dynamic selection of the intention to be achieved next and the appropriate strategy to achieve it whereas guidelines help in the operationalization of the selected intention. The paper presents the map and guidelines and exemplifies the approach with the CREWS-L'Ecritoire method for requirements engineering

Semantically Enhanced Software Traceability Using Deep Learning Techniques

In most safety-critical domains the need for traceability is prescribed by certifying bodies. Trace links are generally created among requirements, design, source code, test cases and other artifacts, however, creating such links manually is time consuming and error prone. Automated solutions use information retrieval and machine learning techniques to generate trace links, however, current techniques fail to understand semantics of the software artifacts or to integrate domain knowledge into the tracing process and therefore tend to deliver imprecise and inaccurate results. In this paper, we present a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into the tracing solution. We propose a tracing network architecture that utilizes Word Embedding and Recurrent Neural Network (RNN) models to generate trace links. Word embedding learns word vectors that represent knowledge of the domain corpus and RNN uses these word vectors to learn the sentence semantics of requirements artifacts. We trained 360 different configurations of the tracing network using existing trace links in the Positive Train Control domain and identified the Bidirectional Gated Recurrent Unit (BI-GRU) as the best model for the tracing task. BI-GRU significantly out-performed state-of-the-art tracing methods including the Vector Space Model and Latent Semantic Indexing.Comment: 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE

Leveraging Intermediate Artifacts to Improve Automated Trace Link Retrieval

Software traceability establishes a network of connections between diverse artifacts such as requirements, design, and code. However, given the cost and effort of creating and maintaining trace links manually, researchers have proposed automated approaches using information retrieval techniques. Current approaches focus almost entirely upon generating links between pairs of artifacts and have not leveraged the broader network of interconnected artifacts. In this paper we investigate the use of intermediate artifacts to enhance the accuracy of the generated trace links – focus- ing on paths consisting of source, target, and intermediate artifacts. We propose and evaluate combinations of techniques for computing semantic similarity, scaling scores across multiple paths, and aggregating results from multiple paths. We report results from five projects, including one large industrial project. We find that leverag- ing intermediate artifacts improves the accuracy of end-to-end trace retrieval across all datasets and accuracy metrics. After further analysis, we discover that leveraging intermediate artifacts is only helpful when a project’s artifacts share a common vocabulary, which tends to occur in refinement and decomposition hierarchies of artifacts. Given our hybrid approach that integrates both direct and transitive links, we observed little to no loss of accuracy when intermediate artifacts lacked a shared vocabulary with source or target artifacts

Managing Requirement Volatility in an Ontology-Driven Clinical LIMS Using Category Theory. International Journal of Telemedicine and Applications

Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.Comment: 36 Pages, 16 Figure