Anh Nguyen-Duc

Hunter-gatherer cycle: a conceptual model of the evolution of software startups

Towards an Early Stage Software Startups Evolution Model

Start-Ups Must Be Ready to Pivot

Security challenges in IoT development: a software engineering perspective

A preliminary study of agility in business and production: cases of early-stage hardware startups

[Context] Advancement in technologies, popularity of small-batch manufacturing and the recent trend of investing in hardware startups are among the factors leading to the rise of hardware startups nowadays. It is essential for hardware startups, companies that involve both software and hardware development, to be not only agile to develop their business but also efficient to develop the right products. [Objective] We investigate how hardware startups achieve agility when developing their products in early stages. [Methods] A qualitative research is conducted with data from 20 hardware startups. [Result] Preliminary results show that agile development is known to hardware entrepreneurs, however it is limitedly adopted. We also found four categories of tactics: (1) strategy, (2) personnel, (3) artifact and (4) resource that enable hardware startups agile in their early stage business and product development. [Conclusions] Agile methodologies should be adopted with the consideration of specific features of hardware development, such as up-front design and vendor dependencies.

The Essence Theory of Software Engineering - Large-Scale Classroom Experiences from 450+ Software Engineering BSc Students

Software Engineering as an industry is highly diverse in terms of development methods and practices. Practitioners employ a myriad of methods and tend to further tailor them by e.g. omitting some practices or rules. This diversity in development methods poses a challenge for software engineering education, creating a gap between education and industry. General theories such as the Essence Theory of Software Engineering can help bridge this gap by presenting software engineering students with higher-level frameworks upon which to build an understanding of software engineering methods and practical project work. In this paper, we study Essence in an educational setting to evaluate its usefulness for software engineering students while also investigating barriers to its adoption in this context. To this end, we observe 102 student teams utilize Essence in practical software engineering projects during a semester long, project-based course.

Do software firms collaborate or compete? A model of coopetition in community-initiated OSS projects

[Background] An increasing number of commercial firms are participating in Open Source Software (OSS) projects to reduce their development cost and increase technical innovativeness. When collaborating with other firms whose sought values are conflicts of interests, firms may behave uncooperatively leading to harmful impacts on the common goal. [Aim] This study explores how software firms both collaborate and compete in OSS projects. [Method] We adopted a mixed research method on three OSS projects. [Result] We found that commercial firms participating in community-initiated OSS projects collaborate in various ways across the organizational boundaries. While most of firms contribute little, a small number of firms that are very active and account for large proportions of contributions. We proposed a conceptual model to explain for coopetition among software firms in OSS projects. The model shows two aspects of coopetition can be managed at the same time based on firm gatekeepers. [Conclusion] Firms need to operationalize their coopetition strategies to maximize value gained from participating in OSS projects.

100+ metrics for software startups - A multi-vocal literature review

Metrics can be used by businesses 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 amidst uncertainty and limited resources. However, whereas conventional business metrics and software metrics have been studied in the past, metrics in the spe-cific context of software startup are not widely covered within academic literature. To promote research in this area and to create a starting point for it, we have conducted a multi-vocal literature review focusing on practitioner literature in order to compile a list of metrics used by software startups. Said list is intended to serve as a basis for further research in the area, as the metrics in it are based on suggestions made by practitioners and not empirically verified.

A Tool-Based Approach for Essentializing Software Engineering Practices

Software Engineers work using highly diverse methods and practices, and general theories in software engineering are lacking. A recent attempt at creating a common ground in the area of software engineering methodologies has been the Essence Theory of Software Engineering. Essence is a method-agnostic progress management framework and a meta-method for Software Engineering (SE). However, tooling for Essence is still lacking. Without dedicated tools and other instruments, a meta-method such as Essence is cumbersome to utilize by practitioners and students. Indeed, Essence currently suffers from a lack of widespread practitioner adoption. In this paper, we thus present an Open Source tool for essentializing methods and practices: Essencery. We conduct a qualitative evaluation of the tool through a quasi-formal experiment and a set of semi-structured interviews. Based on this data, we improve Essencery iteratively before it is utilized in a large-scale project-based course as a proof of concept.

Software Business: 10th International Conference, ICSOB 2019, Jyväskylä, Finland, November 18–20, 2019, Proceedings

Continuous experimentation on artificial intelligence software: a research agenda

Moving from experiments to industrial level AI software development requires a shift from understanding AI/ ML model attributes as a standalone experiment to know-how integrating and operating AI models in a large-scale software system. It is a growing demand for adopting state-of-the-art software engineering paradigms into AI development, so that the development efforts can be aligned with business strategies in a lean and fast-paced manner. We describe AI development as an “unknown unknown” problem where both business needs and AI models evolve over time. We describe a holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products. From this, five areas of challenges are presented with the focus on experimentation. In the end, we propose a research agenda with seven questions for future studies.

Software Startup ESSENCE – How Should Software Startups Work?