An extension of iStar for Machine Learning requirements by following the PRISE methodology

The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains.This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modeling and analytics, funded by the Spanish Ministry of Science and Innovation. And the BALLADEER (PROMETEO/2021/088) project, a Big Data analytical platform for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana). A. Reina-Reina (I-PI 13/20) hold Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Uma Abordagem de Engenharia de Requisitos para Inclusão de Género

Embora a igualdade de género seja um direito humano, continuam a existir situações onde os indivíduos são discriminados com base no seu género. Numa sociedade onde a mulher tem assumido cada vez mais um papel ativo, existe a necessidade de garantir que esta não lide diariamente com situações discriminatórias que possam ter impactos negati- vos na sua vida. A introdução da tecnologia na sociedade e a sua constante evolução tem contribuído para alterar a forma como os indivíduos se relacionam e os seus comporta- mentos. Assim, é importante que a produção de tecnologia não perpetue a discriminação de género, algo que tem elevados impactos na sociedade e, em particular, para os géneros discriminados. No caso específico do software, é necessário assegurar que este é inclusivo e que não discrimina os seus utilizadores com base em características individuais, mais especificamente, com base no género. É comum considerar-se que o software é neutro em relação ao género, ou seja, que se adequa e serve igualmente aos diferentes utilizado- res. Contudo, diversos estudos identificaram que o software tende a beneficiar um dos géneros, mais especificamente, o género masculino. Com o objetivo de criar uma técnica que permita o desenvolvimento de software justo e inclusivo, este trabalho de mestrado propõe uma abordagem de requisitos que ajuda a identificar requisitos de género e que tem como base o modelo conceptual de inclusão de género e o método GenderMag. Para demonstrar o processo de utilização da abordagem, esta foi aplicada à plataforma online Airbnb. A abordagem foi ainda avaliada através de um formulário para o qual foi realizada uma análise quantitativa e qualitativa dos resultados obtidos.Although gender equality is a human right, there are still situations where people are discriminated against because of their gender. In a society where women have taken on an increasingly active role, it is important to ensure that they do not face discriminatory situations that negatively impact their lives. The introduction of technology in society and its constant evolution have helped to change the way people behave and interact with each other. Therefore, it is important that technology production does not perpetuate gender discrimination, which has a significant negative impact on society and especially on the genders that are discriminated against. In the specific case of software, it is important to ensure that it is inclusive and does not discriminate against its users on the basis of individual characteristics, especially gender. It is still common to consider that software gender-neutral, i.e., it is assumed to be suitable for different users and serve them all equally. However, several studies have shown that software tends to benefit one gender, more specifically the male gender. With the aim of creating a technique that allows for fair and inclusive software devel- opment, this master thesis proposes a requirements approach that helps to identify gender requirements. This approach is based on the conceptual model of gender inclusion and the GenderMag method. To demonstrate the process of applying the approach, it was ap- plied to an application called Airbnb. The approach was also evaluated through an online form, for which a quantitative and qualitative analysis of the results obtained was carried out