Ethics in AI through the Developer's View: A Grounded Theory Literature Review

The term ethics is widely used, explored, and debated in the context of developing Artificial Intelligence (AI) based software systems. In recent years, numerous incidents have raised the profile of ethical issues in AI development and led to public concerns about the proliferation of AI technology in our everyday lives. But what do we know about the views and experiences of those who develop these systems: the AI developers? We conducted a grounded theory literature review (GTLR) of 38 primary empirical studies that included AI developers' views on ethics in AI and analysed them to derive five categories - developer awareness, perception, need, challenge, and approach. These are underpinned by multiple codes and concepts that we explain with evidence from the included studies. We present a taxonomy of ethics in AI from developers' viewpoints to assist AI developers in identifying and understanding the different aspects of AI ethics. The taxonomy provides a landscape view of the key aspects that concern AI developers when it comes to ethics in AI. We also share an agenda for future research studies and recommendations for developers, managers, and organisations to help in their efforts to better consider and implement ethics in AI.Comment: 40 pages, 5 figures, 4 table

Software Engineers Response to Public Crisis: Lessons Learnt from Spontaneously Building an Informative COVID-19 Dashboard

The Coronavirus disease 2019 (COVID-19) outbreak quickly spread around the world, resulting in over 240 million infections and 4 million deaths by Oct 2021. While the virus is spreading from person to person silently, fear has also been spreading around the globe. The COVID-19 information from the Australian Government is convincing but not timely or detailed, and there is much information on social networks with both facts and rumors. As software engineers, we have spontaneously and rapidly constructed a COVID-19 information dashboard aggregating reliable information semi-automatically checked from different sources for providing one-stop information sharing site about the latest status in Australia. Inspired by the John Hopkins University COVID-19 Map, our dashboard contains the case statistics, case distribution, government policy, latest news, with interactive visualization. In this paper, we present a participant's in-person observations in which the authors acted as founders of https://covid-19-au.com/ serving more than 830K users with 14M page views since March 2020. According to our first-hand experience, we summarize 9 lessons for developers, researchers and instructors. These lessons may inspire the development, research and teaching in software engineer aspects for coping with similar public crises in the future

Use and misuse of the term "Experiment" in mining software repositories research

The significant momentum and importance of Mining Software Repositories (MSR) in Software Engineering (SE) has fostered new opportunities and challenges for extensive empirical research. However, MSR researchers seem to struggle to characterize the empirical methods they use into the existing empirical SE body of knowledge. This is especially the case of MSR experiments. To provide evidence on the special characteristics of MSR experiments and their differences with experiments traditionally acknowledged in SE so far, we elicited the hallmarks that differentiate an experiment from other types of empirical studies and characterized the hallmarks and types of experiments in MSR. We analyzed MSR literature obtained from a small-scale systematic mapping study to assess the use of the term experiment in MSR. We found that 19% of the papers claiming to be an experiment are indeed not an experiment at all but also observational studies, so they use the term in a misleading way. From the remaining 81% of the papers, only one of them refers to a genuine controlled experiment while the others stand for experiments with limited control. MSR researchers tend to overlook such limitations, compromising the interpretation of the results of their studies. We provide recommendations and insights to support the improvement of MSR experiments.This work has been partially supported by the Spanish project: MCI PID2020-117191RB-I00.Peer ReviewedPostprint (author's final draft

Dione: An Integrated Measurement and Defect Prediction Solution

We present an integrated measurement and defect prediction tool: Dione. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple tools with incompatible interfaces in order to deploy a complete measurement and prediction solution. Dione provides a fully integrated solution where data extraction, defect prediction and reporting steps fit seamlessly. In this paper, we present the major functionality and architectural elements of Dione followed by an overview of our demonstration

Adopción de Metodologías Ágiles: un estudio comparativo entre España y Europa

El objetivo de este estudio es analizar el estado de la adopción de metodologías ágiles en la industria software española comparándolo con la europea. Se han empleado cuestionarios, tanto en el contexto ágil como en el convencional, para evaluar el uso de diferentes metodologías y prácticas ágiles, estrategias empleadas en el proceso de adopción, factores que motivan su uso, así como beneficios que reportan y limitaciones y retos que implican su aplicación. En el entorno español, el estudio se realizó utilizando una muestra de organizaciones que participaron en el último Agile Open Spain (2009). A nivel europeo, la encuesta se llevó a cabo en organizaciones del proyecto Flexi, pioneras en la adopción de metodologías ágiles a nivel mundial. La comparación de resultados muestra diferencias interesantes en el proceso de adopción

How Good Are My Tests?

Background: Test quality is a prerequisite for achieving production system quality. While the concept of quality is multidimensional, most of the effort in testing context hasbeen channelled towards measuring test effectiveness. Objective: While effectiveness of tests is certainly important, we aim to identify a core list of testing principles that also address other quality facets of testing, and to discuss how they can be quantified as indicators of test quality. Method: We have conducted a two-day workshop with our industry partners to come up with a list of relevant principles and best practices expected to result in high quality tests. We then utilised our academic and industrial training materials together with recommendations in practitioner oriented testing books to refine the list. We surveyed existing literature for potential metrics to quantify identified principles. Results: We have identified a list of 15 testing principles to capture the essence of testing goals and best practices from quality perspective. Eight principles do not map toexisting test smells and we propose metrics for six of those. Further, we have identified additional potential metrics for the seven principles that partially map to test smells. Conclusion: We provide a core list of testing principles along with a discussion of possible ways to quantify them for assessing goodness of tests. We believe that our work would be useful for practitioners in assessing the quality of their tests from multiple perspectives including but not limited to maintainability, comprehension and simplicity