Software startup engineering: A systematic mapping study

Berg, Vebjørn; Birkeland, Jørgen; Nguyen-Duc, Anh; Pappas, Ilias O.; Jaccheri, Letizia

doi:10.1016/j.jss.2018.06.043

Cited by 100 publications

(97 citation statements)

References 52 publications

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“…Innovation and startup driven publications make up a smaller chunk of interest, and this might be particularly critical for SEE following industry trends [19] as the validity to industry of the courses should be continuously addressed. Similar mapping studies [69] showed the present startup diffusion and challenges in industry.…”

Section: Discussionsupporting

confidence: 75%

Industry Trends in Software Engineering Education: A Systematic Mapping Study

Cico

Jaccheri

2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Contemporary approaches and trends in software engineering courses have been continuously updated over the last four decades. Adaptation to industry needs is crucial for future educational purposes and vice versa. Tech startups have become a driving force in the economy and a major industry trend. The goal of this paper is first to critically assess how contemporary industry trends and in particular, tech startups have influenced the updating of software engineering curricula. The second goal is to evaluate the contribution of industry and in particular, tech startups, processes and models in present learning approaches. The third goal addresses how stakeholders have helped in the infusion of industry trends and in particular, in tech startup approaches in academia. This study is a systematic literature mapping. A total of 138 papers were selected based on education goals, research, and contribution type. Of the primary education topics, 78% were related to teaching strategies, 9% to globalization and training methods, and less than 5% to tech startup and industry innovation. Common stakeholders accounting for the change are students, researchers, and lecturers and project managers, product owners, customers, and clients from industry. This study showed that industrial models or methods involving Agile and Scrum have been widely adopted. Less investigated areas that have recently become common industry trends, such as tech startup models adopting lean methodology, require further attention and might create opportunities for updating the curricula. We discuss future model possibilities for exploiting tech startups as a means for renewing future capstone courses. Index Terms-software engineering courses, teaching approaches, mapping study, tech startups

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Section: Discussionsupporting

confidence: 75%

Industry Trends in Software Engineering Education: A Systematic Mapping Study

Cico

Jaccheri

2019

2019 IEEE/ACM 41st International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Self Cite

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“…Customer feedback highlights new functionality and improvements. As long-term planning is infeasible in the chaotic environment of startups, flexibility and reactiveness are necessary [18].…”

Section: Product Development In Startupsmentioning

confidence: 99%

The Role of Data Analytics in Startup Companies: Exploring Challenges and Barriers

Berg

Birkeland

Pappas

et al. 2018

Lecture Notes in Computer Science

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The advancement in technology is transforming societies into digital arenas and paves the way towards the achievement of digital transformation. With every transaction in the digital world leading to the generation of data, big data and their analytics have received major attention in various fields and different contexts, examining how they may benefit the different actors in the society. The present study aims to identify how startups that develop products with both software and hardware parts can generate value from data analytics and what challenges they face towards this direction. To this end, we performed a multiple-case study with early-stage startups and employed qualitative analysis on a dataset from 13 startups. Through semi-structured interviews, we examine how these companies use data analytics. The findings show that although the benefits from data analytics are clear, multiple barriers and challenges exist for the startups to be able to create value from them. The major ones are about their resources, including human skills, economical resources, as well as time management and privacy issues.

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“…Software start-ups are small companies created to build and market a software-intensive product [1]. Start-ups are characterized by rapid evolution, small teams, uncertainty about customer needs and market conditions, and a high failure rate [2], [3]. However, leveraging cutting-edge technologies, risk, and speed, start-ups can launch software products fast [4], [5].…”

Section: Introductionmentioning

confidence: 99%

A Progression Model of Software Engineering Goals, Challenges, and Practices in Start-Ups

Klotins

Unterkalmsteiner

Chatzipetrou

et al. 2021

IIEEE Trans. Software Eng.

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Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among startups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors.

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Software startup engineering: A systematic mapping study

Cited by 100 publications

References 52 publications

Industry Trends in Software Engineering Education: A Systematic Mapping Study

Industry Trends in Software Engineering Education: A Systematic Mapping Study

The Role of Data Analytics in Startup Companies: Exploring Challenges and Barriers

A Progression Model of Software Engineering Goals, Challenges, and Practices in Start-Ups

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