data science

An Empirical Study on Female Participation in Software Project Courses

Gender issues in software engineering education are gaining research attention due to the desire to promote female participation in the field. The objective of this work is to enhance the understanding of female students' participation in software engineering projects to support gender-aware course optimization. Since 2015, we have investigated the participation of female students in terms of software engineering activities and team dynamics in a software project course that involves a real customer. We found that female students are more active with project management and requirement engineering, while they remain under-represented in highly complex or specific tasks, i.e. architecture work, and user experience design. We found no statistically significant difference in perceived team dynamics between male and female students. Insights on female project activities would facilitate the arrangement of project teams so that learning can be distributed equally across genders

Appreciation to Empirical Software Engineering Reviewers of 2018

Hypotheses Engineering: First Essential Steps of Experiment-Driven Software Development

Recent studies have proposed the use of experiments to guide software development in order to build features that the user really wants. Some authors argue that this approach represents a new way to develop software that is different from the traditional requirement-driven one. In this position paper, we propose the discipline of Hypotheses Engineering in comparison to Requirements Engineering, highlighting the importance of proper handling hypotheses that guide experiments. We derive a set of practices within this discipline and present how the literature has tackled them up to now. Finally, we propose a set of research questions that could guide future work towards helping practitioners.

Ethically Aligned Design: An Empirical Evaluation of the RESOLVEDD-Strategy in Software and Systems Development Context

Use of artificial intelligence (AI) in human contexts calls for ethical considerations for the design and development of AI-based systems. However, little knowledge currently exists on how to provide useful and tangible tools that could help software developers and designers implement ethical considerations into practice. In this paper, we empirically evaluate a method that enables ethically aligned design in a decision-making process. Though this method, titled the RESOLVEDD strategy, originates from the field of business ethics, it is being applied in other fields as well. We tested the RESOLVEDD strategy in a multiple case study of five student projects where the use of ethical tools was given as one of the design requirements. A key finding from the study indicates that simply the presence of an ethical tool has an effect on ethical consideration, creating more responsibility even in instances where the use of the tool is not intrinsically motivated.

Implementing Ethics in AI: Initial Results of an Industrial Multiple Case Study

Towards a Secure DevOps Approach for Cyber-Physical Systems: An Industrial Perspective

With the expansion of cyber-physical systems (CPSs) across critical and regulated industries, systems must be continuously updated to remain resilient. At the same time, they should be extremely secure and safe to operate and use. The DevOps approach caters to business demands of more speed and smartness in production, but it is extremely challenging to implement DevOps due to the complexity of critical CPSs and requirements from regulatory authorities. In this study, expert opinions from 33 European companies expose the gap in the current state of practice on DevOps-oriented continuous development and maintenance. The study contributes to research and practice by identifying a set of needs. Subsequently, the authors propose a novel approach called Secure DevOps and provide several avenues for further research and development in this area. The study shows that, because security is a cross-cutting property in complex CPSs, its proficient management requires system-wide competencies and capabilities across the CPSs development and operation.

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

Various recent Artificial Intelligence (AI) system failures, some of which have made the global headlines, have highlighted issues in these systems. These failures have resulted in calls for more ethical AI systems that better take into account their effects on various stakeholders. However, implementing AI ethics into practice is still an on-going challenge. High-level guidelines for doing so exist, devised by governments and private organizations alike, but lack practicality for developers. To address this issue, in this paper, we present a method for implementing AI ethics. The method, ECCOLA, has been iteratively developed using a cyclical action design research approach. The method aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Startup Metrics That Tech Entrepreneurs Need to Know

Metrics can be used by firms to make more objective decisions based on data. Software startups in particular are characterized by the uncertain or even chaotic nature of the contexts in which they operate. Using data in the form of metrics can help software startups to make the right decisions amid uncertainty and limited resources. However, whereas conventional business metrics and software metrics have been studied in the past, metrics in the specific context of software startups have not been studied. In this chapter, we present the results of a multivocal literature review to offer you 118 metrics practitioner experts think software startups should measure. These metrics can give you ideas for what your startup should measure.

A Multiple Case Study of Artificial Intelligent System Development in Industry

There is a rapidly increasing amount of Artificial Intelligence (AI) systems developed in recent years, with much expectation on its capacity of innovation and business value generation. However, the promised value of AI systems in specific business contexts might not be understood, and further integrated into the development processes. We wanted to understand how software engineering processes and practices can be applied to develop AI systems in a fast-faced, business-driven manner. As the first step, we explored contextual factors of AI development and the connections between AI developments to business opportunities. We conducted 12 semi-structured interviews in seven companies in Brazil, Norway and Southeast Asia. Our investigation revealed different types of AI systems and different AI development approaches. However, it is common that business opportunities involving with AI systems are not validated and there is lack of business-driven metrics that guide the development of AI systems. The findings have implications for future research on business-driven AI development and supporting tools and practices.

Software Startup Practices - Software Development in Startups Through the Lens of the Essence Theory of Software Engineering

. Software startups continue to be important drivers of economy globally. As the initial investment required to found a new software company becomes smaller and smaller resulting from technological advances such as cloud technology, increasing numbers of new software startups are born. Startups are considered to differ from other types of software organizations in various ways, including software development. In this paper, we study software development in startups from the point of view of practices to better understand how startups develop software. Using extant literature and case study data, we devise a list of practices which we categorize using the Essence Theory of Software Engineering (Essence). Based on the data, we propose a list of common practices utilized by software startups. Additionally, we propose potential changes to Essence to make it better suited for the software startup context.

The entrepreneurial logic of startup software development: A study of 40 software startups

Software startups are an essential source of innovation and software-intensive products. The need to understand product development in startups and to provide relevant support are highlighted in software research. While state-of-the-art literature reveals how startups develop their software, the reasons why they adopt these activities are underexplored. This study investigates the tactics behind software engineering (SE) activities by analyzing key engineering events during startup journeys. We explore how entrepreneurial mindsets may be associated with SE knowledge areas and with each startup case. Our theoretical foundation is based on causation and effectuation models. We conducted semi-structured interviews with 40 software startups. We used two-round open coding and thematic analysis to describe and identify entrepreneurial software development patterns. Additionally, we calculated an effectuation index for each startup case. We identified 621 events merged into 32 codes of entrepreneurial logic in SE from the sample. We found a systemic occurrence of the logic in all areas of SE activities. Minimum Viable Product (MVP), Technical Debt (TD), and Customer Involvement (CI) tend to be associated with effectual logic, while testing activities at different levels are associated with causal logic. The effectuation index revealed that startups are either effectuation-driven or mixed-logics-driven. Software startups fall into two types that differentiate between how traditional SE approaches may apply to them. Effectuation seems the most relevant and essential model for explaining and developing suitable SE practices for software startups.

Software - A New Open Access Journal on Software Engineering

Software (ISSN: 2674-113X) [...]