AQCLab is a laboratory for software and data quality evaluation in conformance to the ISO/IEC 25000 series of standards. As such, requirements are a fundamental element in the evaluations that are carried out, as they are the input for software Functional Suitability and Data Quality, two of the types of evaluation carried out by the laboratory. Software Maintainability evaluations are also carried out by the laboratory, where the requirements on applicable metrics and thresholds to be met have been established by the laboratory.Furthermore, the laboratory's personnel possess considerable expertise in the domain of auditing software lifecycle processes, which has enabled them to observe how requirements are managed in multiple companies.In this article, we present the experience of AQCLab over the years, in the capacity of software product and data quality evaluators and software development process auditors, with regard to the manner in which our clients manage and define requirements. This has led us to the realization that these practices are often neglected.

In an era dominated by technological integration, Artificial Intelligence (AI) is pivotal across various sectors, driving significant advancements and demanding robust quality measures for its implementations. This paper introduces a novel AI maturity assessment framework designed in alignment with International Standards provided by ISO (International Organization for Standardization) and IEC (International Electrotechnical Commission), specifically with the ISO/IEC 33000 family of standards for process assessment. Our framework aims to provide AI system developers with a structured tool for continuous improvement of their development processes, thereby enhancing the reliability and efficacy of AI applications. To demonstrate the applicability of our proposed framework, we have validated it through a case study in the automotive sector. Specifically, the framework was employed to assess and enhance an AI project to develop a mechanism to determine vehicle behavior from sensor data within the constraints of onboard devices. Our findings identify key improvement points contributing to the iterative enhancement of AI system quality in engineering applications.

Quantum computing is no longer a promise of the future but a rapidly evolving reality. Advances in quantum hardware are making it possible to make tangible a computational reality that until now was only theoretical. The proof of this is that development languages and platforms are appearing that bring physical principles closer to developers, making it feasible to begin to propose, in different areas of society, solutions to problems that until now were unsolvable. However, security vulnerabilities are also emerging that could hinder the progress of quantum computing, as well as its transition and development in industry. For this reason, this article proposes a review of some of the first artefacts that are emerging in the field of quantum computing. From this analysis, we begin to identify possible security issues that could become potential vulnerabilities in the quantum software of tomorrow. Likewise, and following the experience in classical software development, the testing technique is analysed as a possible candidate for improving security in quantum software development. Following the principles of Quantum Software Engineering, we are aware of the lack of tools, techniques and knowledge necessary to guarantee the development of quantum software in the immediate future. Therefore, this article aims to offer some first clues on what would be a roadmap to guarantee secure quantum software development.

Data quality evaluation is built upon data quality measurement results. “Data quality evaluation” uses the “data quality rules” representing the risk appetite of the organization to decide on the usability of the data; “data quality measurement” uses the business rules describing the “data requirements” or “data specifications” to determine the validity of the data. Consequently, to conduct meaningful and useful data quality evaluations, business rules must be first completely identified and captured at the beginning of the evaluation to perform sound measurements. We propose that the evaluation leads to better and more interpretable and useful results when the potential contribution of these business rules to the measurement of the data quality characteristics is first evaluated, avoiding the inclusion in the evaluation of those not having potential contribution and the resulting waste of resources. Considering this, we feel that for a better management of business rules for data quality evaluation, it makes sense to group all business rules having an important contribution to the evaluation of data quality characteristics, something that other business rules management methodologies have not covered yet. Through our experiences in conducting industrial projects of data quality evaluations we identified six problems when collecting and grouping the business rules. These problems make data quality evaluation processes less efficient and more costly. The main contribution of this paper is a methodology to systematically collect, group and validate the business rules to avoid or to alleviate these problems. For the sake of generalization, comparability, and reusability, we propose to do the grouping for data quality characteristics and properties defined in ISO/IEC 25012 and ISO/IEC 25024, respectively. Lastly, we validate the methodology in three case studies of real projects. From this validation, it is possible to raise the conclusion that the methodology is useful, applicable in the real world, and valid to capture and group the business rules used as a basis for data quality evaluation.

Sustainability is not a trend; more and more areas of knowledge defend it and apply it among their main characteristics. The area of Information Technology (IT) has not been left behind and, through the so-called Green IT, advocates for a more sustainable environment. Organizations around the world are realizing that Green IT is an important asset that produces great benefits at ecological, social, and economic level. For this reason, we have developed a revised version of the “Governance and Management Framework for Green IT”. With this new version we intend to offer a more complete and solid guide that helps organizations to gradually implement, assess, and improve those characteristics of governance and management that are the basis of the processes and practices of Green IT. This proposal has been empirically validated through a case study and the results obtained demonstrate the validity, usefulness, and applicability of the framework for organizations.

Sustainability is a problem that is increasingly worrying organisations around the world, which has led them to adopt sustainable practices in their processes. From the point of view of one of the areas that have had the greatest impact in recent times in organisations, the area of information technology (IT), different sustainable practices have arisen in isolation, known as Green IT practices. So, a framework that organises and establishes these practices (until now isolated) is necessary in order to implement, assess, and improve the Green IT in organisations in an efficient, gradual, and integrated way. In this study, the authors propose a maturity model based on capability maturity model integration (CMMI), through which it is intended to help organisations gradually implement and improve through different levels of maturity, the governance and management of Green IT. The validations of this proposal carried out through experts and practical cases demonstrate the usefulness of this proposal when implementing, assessing, and improving the Green IT in organisations.

Scrum dramatically shortens the feedback loop between customer and developers as well as between requirement list and functional implementations. Scrum poses to turn small teams into self-managers of their own work. Despite the increasing importance of Scrum in the software development Industry, Academia is not providing a suitable response to meet the demand of professionals. This is because, in many cases, reference curricula do not consider Scrum with a great relevance in knowledge areas nor in time scheduled for it. Even so, Academia should be able to educate and provide software engineers with enough Scrum knowledge and skills. To collaborate to this aim, we present our efforts for improving the teaching/learning experience when dealing with time limitations: we have verified that when using a comparison strategy (regarding traditional software development methodologies like Unified Process, (UP)), students improve their learning process. To validate our results, students were assessed through a twofold assessment: (i) based on Scrum certification exams plus, and with (ii) a questionnaire based on the previously mentioned comparison (Scrum vs UP). The main conclusion raised from this investigation is that students' learning experience is really more satisfactory when Scrum concepts are introduced after a comparison with UP ones.

The installation of Intelligent Pavement (IPavement) in cities highlights the obvious need for the development of software services that can be offered by this technology. These services should be developed in conformance with international quality standards such as ISO/IEC 25000, which make it possible to give assurance that the services must established quality requirements. This paper therefore presents the environment for the quality certification of the services developed for the IPavement, created by the authors. This environment is formed by an assessment process, a quality model, and set of assessment tools. The results of a case study carried out to evaluate the quality of a service developed for IPavement are also set out; this study has tested the practical application of the environment created and has proven the need to develop tools to assist in the evaluation of the quality IPavement services.