Automated Software Tool Support for Checking the Inconsistency of Requirements

Handling inconsistency in software requirements is a complicated task which has attracted the interest of many groups of researchers. Formal and semi-formal specifications often have inconsistencies in the depicted requirements that need to be managed and resolved. This is particularly challenging when refining informal to formalized requirements. We propose an automated tool with traceability and consistency checking techniques to support analysis of requirements and traceability between different representations: textual, visual, informal and formal

Review of Requirement Engineering Approaches for Software Product Lines

The Software Product Lines (SPL) paradigm is one of the most recent topics of interest for the software engineering community. On the one hand, the Software Product Lines is based on a reuse strategy with the aim to reduce the global time-to-market of the software product, to improve the software product quality, and to reduce the cost. On the other hand, traditional Requirement Engineering approaches could not be appropriated to deal with the new challenges that arises the SPL adoption. In the last years, several approaches have been proposed to cover this limitation. This technical report presents an analysis of specific approaches used in the development of SPL to provide solutions to model variability and to deal with the requirements engineering activities. The obtained results show that most of the research in this context is focused on the Domain Engineering, covering mainly the Feature Modeling and the Scenario Modeling. Among the studied approaches, only one of them supported the delta identification; this fact implies that new mechanisms to incorporate new deltas in the Domain specification are needed. Regarding the SPL adoption strategy, most of the approaches support a proactive strategy. However, this strategy is the most expensive and risk-prone. Finally, most of the approaches were based on modeling requirements with feature models giving less support to other important activities in the requirements engineering process such as elicitation, validation, or verification of requirements. The results of this study provide a wide view of the current state of research in requirements engineering for SPL and also highlight possible research gaps that may be of interest for researchers and practitioners.Blanes Domínguez, D.; Insfrán Pelozo, CE. (2011). Review of Requirement Engineering Approaches for Software Product Lines. http://hdl.handle.net/10251/1023

Requirements Engineering in the Development Process of Web Systems: A Systematic Literature Review

Requirements Engineering (RE) is the first phase in the software development process during which designers attempt to fully satisfy users’ needs. Web Engineering (WE) methods should consider adapting RE to the Web’s large and diverse user groups. The objective of this work is to classify the literature with regard to the RE applied in WE in order to obtain the current “state-of-the-art”. The present work is based on the Systematic Literature Review (SLR) method proposed by Kitchenham; we have reviewed publications from ACM, IEEE, Science Direct, DBLP and World Wide Web. From a population of 3059 papers, we identified 14 primary studies, which provide information concerning RE when used in WE methods.This work has been partially supported by the Programa de Fomento y Apoyo a Proyectos de Investigación (PROFAPI) from the Universidad Autónoma de Sinaloa (México), and the MANTRA project (GRE09-17) from the University of Alicante, Spain, and GV/2011/035 from the Valencia Government

From Bugs to Decision Support – Leveraging Historical Issue Reports in Software Evolution

Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development. Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While the sheer number of incoming issue reports might challenge the overview of a human developer, our techniques instead benefit from the availability of ever-growing training data. We leverage the volume of issue reports to develop accurate decision support for software evolution. We evaluate our proposals both by deploying an RSSE in two development teams, and by simulation scenarios, i.e., we assess the correctness of the RSSEs' output when replaying the historical inflow of issue reports. In total, more than 60,000 historical issue reports are involved in our studies, originating from the evolution of five proprietary systems for two companies. Our results show that RSSEs for both IA and CIA can help developers navigate large software projects, in terms of locating development teams and software artifacts. Finally, we discuss how to support the transfer of our results to industry, focusing on addressing the context dependency of our tool support by systematically tuning parameters to a specific operational setting

Recovering from a Decade: A Systematic Mapping of Information Retrieval Approaches to Software Traceability

Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery

Natural Language Requirements Processing: A 4D Vision

The future evolution of the application of natural language processing technologies in requirements engineering can be viewed from four dimensions: discipline, dynamism, domain knowledge, and datasets

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

Genetic algorithms: a practical approach to generate textual patterns for requirements authoring

The writing of accurate requirements is a critical factor in assuring the success of a project. Text patterns are knowledge artifacts that are used as templates to guide engineers in the requirements authoring process. However, generating a text pattern set for a particular domain is a time-consuming and costly activity that must be carried out by specialists. This research proposes a method of automatically generating text patterns from an initial corpus of high-quality requirements, using genetic algorithms and a separate-and-conquer strategy to create a complete set of patterns. Our results show this method can generate a valid pattern set suitable for requirements authoring, outperforming existing methods by 233%, with requirements ratio values of 2.87 matched per pattern found; as opposed to 1.23 using alternative methods