A Lightweight State Machine for Validating Use Case Descriptions

This paper presents a tool to provide an enaction capability for use case descriptions. Use cases have wide industry acceptance and are well suited for constructing initial approximations of the intended behaviour. However, use case descriptions are still relatively immature with respect to precise syntax and semantics. Hence, despite promising work on providing writing guidelines, rigorous validation of use case descriptions requires further support. One approach to supporting validation is to use enaction. Indeed, enactable models have been used extensively within process modelling to clarify understanding of descriptions. Given the importance of requirements validation, such automated support promises significant benefits. However, the need to produce formal descriptions, to drive enaction, is often seen as a barrier to the takeup of such technologies. That is, developers have traditionally been reluctant to increase the proportion of effort devoted to requirements activities. Our approach involves the development of a lightweight state-machine, which obviates any need to create intermediate formal descriptions, thereby maintaining the simple nature of the use case description. Hence, this 'lightweight' approach, which provides an enaction capability ‘for minimal effort’, increases the likelihood of industrial take-up

Development Process for Multi-Disciplinary Embedded Control Systems

This report contains the progress report for the qualification exam for Industrial PhD student Sune Wolff. Initial work on describing a development process for multi-disciplinary systems using collaborative modelling and co-simulation is described

Integrating Security into the Undergraduate Software Engineering Curriculum

This research included a thorough examination of the existing software assurance or what is commonly called software security knowledge, methodologies and what information security technologies is currently being recommended by the information technology community. Finally it is demonstrated how this security knowledge could be incorporated into the curriculum for undergraduate software engineering

Relevance, benefits, and problems of software modelling and model driven techniques—A survey in the Italian industry

Context Claimed benefits of software modelling and model driven techniques are improvements in productivity, portability, maintainability and interoperability. However, little effort has been devoted at collecting evidence to evaluate their actual relevance, benefits and usage complications. Goal The main goals of this paper are: (1) assess the diffusion and relevance of software modelling and MD techniques in the Italian industry, (2) understand the expected and achieved benefits, and (3) identify which problems limit/prevent their diffusion. Method We conducted an exploratory personal opinion survey with a sample of 155 Italian software professionals by means of a Web-based questionnaire on-line from February to April 2011. Results Software modelling and MD techniques are very relevant in the Italian industry. The adoption of simple modelling brings common benefits (better design support, documentation improvement, better maintenance, and higher software quality), while MD techniques make it easier to achieve: improved standardization, higher productivity, and platform independence. We identified problems, some hindering adoption (too much effort required and limited usefulness) others preventing it (lack of competencies and supporting tools). Conclusions The relevance represents an important objective motivation for researchers in this area. The relationship between techniques and attainable benefits represents an instrument for practitioners planning the adoption of such techniques. In addition the findings may provide hints for companies and universitie

Managing gamified programming courses with the FGPE platform

E-learning tools are gaining increasing relevance as facilitators in the task of learning how to program. This is mainly a result of the pandemic situation and consequent lockdown in several countries, which forced distance learning. Instant and relevant feedback to students, particularly if coupled with gamification, plays a pivotal role in this process and has already been demonstrated as an effective solution in this regard. However, teachers still struggle with the lack of tools that can adequately support the creation and management of online gamified programming courses. Until now, there was no software platform that would be simultaneously open-source and general-purpose (i.e., not integrated with a specific course on a specific programming language) while featuring a meaningful selection of gamification components. Such a solution has been developed as a part of the Framework for Gamified Programming Education (FGPE) project. In this paper, we present its two front-end components: FGPE AuthorKit and FGPE PLE, explain how they can be used by teachers to prepare and manage gamified programming courses, and report the results of the usability evaluation by the teachers using the platform in their classes.info:eu-repo/semantics/publishedVersio

Quality measures and assurance for AI (Artificial Intelligence) software

This report is concerned with the application of software quality and evaluation measures to AI software and, more broadly, with the question of quality assurance for AI software. Considered are not only the metrics that attempt to measure some aspect of software quality, but also the methodologies and techniques (such as systematic testing) that attempt to improve some dimension of quality, without necessarily quantifying the extent of the improvement. The report is divided into three parts Part 1 reviews existing software quality measures, i.e., those that have been developed for, and applied to, conventional software. Part 2 considers the characteristics of AI software, the applicability and potential utility of measures and techniques identified in the first part, and reviews those few methods developed specifically for AI software. Part 3 presents an assessment and recommendations for the further exploration of this important area