On the Presence of Green and Sustainable Software Engineering in Higher Education Curricula

Nowadays, software is pervasive in our everyday lives. Its sustainability and environmental impact have become major factors to be considered in the development of software systems. Millennials-the newer generation of university students-are particularly keen to learn about and contribute to a more sustainable and green society. The need for training on green and sustainable topics in software engineering has been reflected in a number of recent studies. The goal of this paper is to get a first understanding of what is the current state of teaching sustainability in the software engineering community, what are the motivations behind the current state of teaching, and what can be done to improve it. To this end, we report the findings from a targeted survey of 33 academics on the presence of green and sustainable software engineering in higher education. The major findings from the collected data suggest that sustainability is under-represented in the curricula, while the current focus of teaching is on energy efficiency delivered through a fact-based approach. The reasons vary from lack of awareness, teaching material and suitable technologies, to the high effort required to teach sustainability. Finally, we provide recommendations for educators willing to teach sustainability in software engineering that can help to suit millennial students needs.Comment: The paper will be presented at the 1st International Workshop on Software Engineering Curricula for Millennials (SECM2017

AI-enabled Automation for Completeness Checking of Privacy Policies

Technological advances in information sharing have raised concerns about data protection. Privacy policies contain privacy-related requirements about how the personal data of individuals will be handled by an organization or a software system (e.g., a web service or an app). In Europe, privacy policies are subject to compliance with the General Data Protection Regulation (GDPR). A prerequisite for GDPR compliance checking is to verify whether the content of a privacy policy is complete according to the provisions of GDPR. Incomplete privacy policies might result in large fines on violating organization as well as incomplete privacy-related software specifications. Manual completeness checking is both time-consuming and error-prone. In this paper, we propose AI-based automation for the completeness checking of privacy policies. Through systematic qualitative methods, we first build two artifacts to characterize the privacy-related provisions of GDPR, namely a conceptual model and a set of completeness criteria. Then, we develop an automated solution on top of these artifacts by leveraging a combination of natural language processing and supervised machine learning. Specifically, we identify the GDPR-relevant information content in privacy policies and subsequently check them against the completeness criteria. To evaluate our approach, we collected 234 real privacy policies from the fund industry. Over a set of 48 unseen privacy policies, our approach detected 300 of the total of 334 violations of some completeness criteria correctly, while producing 23 false positives. The approach thus has a precision of 92.9% and recall of 89.8%. Compared to a baseline that applies keyword search only, our approach results in an improvement of 24.5% in precision and 38% in recall

UML diagram synthesis techniques: a systematic mapping study

Context: The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard modeling language for object-oriented modeling and documentation. Since the various UML diagrams describe different aspects of one, and only one, software under development, they are not independent but strongly depend on each ot her in many ways. In other words, diagrams must remain consistent. Dependencies between diagrams can become so intricate that it is sometimes even possible to synthesize one diagram on the basis of others. Support for synthesizing one UML diagram from other diagrams can provide the designer with significant help, thus speeding up the design process, decreasing the risk of errors, and guaranteeing consistency among the diagrams. Objective: The aim of this article is to provide a comprehensive summary of UML synthesis techniques as they have been described in literature to date in order to obtain an extensive and detailed overview of the current research in this area. Method: We have performed a Systematic Mapping Study by following well-known guide-lines. We selected ten primary studies by means of a

A systematic identification of consistency rules for UML diagrams

UML diagrams describe different views of one piece of software. These diagrams strongly depend on each other and must therefore be consistent with one another, since inconsistencies between diagrams may be a source of faults during software development activities that rely on these diagrams. It is therefore paramount that consistency rules be defined and that inconsistencies be detected, analyzed and fixed. The relevant literature shows that authors typically define their own UML consistency rules, sometimes defining the same rules and sometimes defining rules that are already in the UML standard. The reason might be that no consolidated set of rules that are deemed relevant by authors can be found to date. The aim of our research is to provide a consolidated set of UML consistency rules and obtain a detailed overview of the current research in this area. We therefore followed a systematic procedure in order to collect and analyze UML consistency rules. We then consolidated a set of 116 UML consistency rules (avoiding redundant definitions or definitions already in the UML standard) that can be used as an important r

UML consistency rules: a systematic mapping study

Context: The Unified Modeling Language (UML), with its 14 different diagram types, is the de-facto standard tool for objectoriented modeling and documentation. Since the various UML diagrams describe different aspects of one, and only one, software under development, they are not independent but strongly depend on each other in many ways. In other words, the UML diagrams describing a software must be consistent. Inconsistencies between these diagrams may be a source of the considerable increase of faults in software systems. It is therefore paramount that these inconsistencies be detected, ana

CQA-Meth: una metodología para la evaluación de la calidad de los modelos software

Las empresas se preocupan cada vez más de la calidad de sus productos software y reconocen que las técnicas de evaluación y aseguramiento de la calidad deberían aplicarse desde las primeras etapas del proceso de desarrollo. Esto se debe a que el foco de atención de la calidad ha pasado del código a los modelos. La calidad de los modelos ha ido ganando cada vez más relevancia desde que apareció el paradigma del Desarrollo Dirigido por Modelos (MDD). Aunque existen algunas metodologías para la evaluación de la calidad de los modelos software, todas son propuestas aisladas que se centran en un modelo en concreto y sólo proponen técnicas de evaluación específicas para ese modelo. No hay ninguna metodología genérica y flexible que permita la evaluación de la calidad de cualquier tipo de modelo software, y mucho menos una herramienta que la soporte. Para abordar este problema, en este artículo proponemos un entorno integrado llamado Entorno CQA (Continuous Quality Assessment), que consiste en una metodología, CQA-Meth, y la herramienta que la implementa, CQA-Tool. CQA-Tool se complementa con un conjunto de herramientas para evaluar la calidad de los distintos modelos UML. Además, CQA-Tool permite construir un catálogo de técnicas de evaluación que integre todas las técnicas de evaluación disponibles (por ejemplo, medidas, listas de comprobación, guías, convenciones de modelado, etc.) para cada modelo UML. El Entorno CQA es apropiado para: (i) empresas que ofrecen servicios de evaluación de la calidad del software, (ii) factorías de software que quieran evaluar sus propios desarrollos, (iii) organizaciones que hayan externalizado una parte o la totalidad de sus desarrollos software y quieran obtener una evaluación independiente de la calidad de los productos software que adquieren.Sociedad Argentina de Informática e Investigación Operativ

On Systematically Building a Controlled Natural Language for Functional Requirements

[Context] Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. [Objective] In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. [Method] We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. [Results] Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. [Conclusion] Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain