Software development in startup companies: A systematic mapping study

• URL: http://arxiv.org/abs/2307.13104v1
• Date: Mon, 24 Jul 2023 19:49:57 GMT
• Title: Software development in startup companies: A systematic mapping study
• Authors: Nicol\`o Paternoster, Carmine Giardino, Michael Unterkalmsteiner, Tony Gorschek, Pekka Abrahamsson
• Abstract summary: This study aims to structure and analyze the literature on software development in startup companies. A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed.
• Score: 4.881718571745022
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present an unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Related papers

• Estimating the Energy Footprint of Software Systems: a Primer [56.200335252600354]
  quantifying the energy footprint of a software system is one of the most basic activities. This document aims to be a starting point for researchers who want to begin conducting work in this area.
  arXiv  Detail & Related papers  (2024-07-16T11:21:30Z)
• Bridging Gaps, Building Futures: Advancing Software Developer Diversity and Inclusion Through Future-Oriented Research [50.545824691484796]
  We present insights from SE researchers and practitioners on challenges and solutions regarding diversity and inclusion in SE. We share potential utopian and dystopian visions of the future and provide future research directions and implications for academia and industry.
  arXiv  Detail & Related papers  (2024-04-10T16:18:11Z)
• Charting a Path to Efficient Onboarding: The Role of Software Visualization [49.1574468325115]
  The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools. This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
  arXiv  Detail & Related papers  (2024-01-17T21:30:45Z)
• A Progression Model of Software Engineering Goals, Challenges, and Practices in Start-Ups [5.664445343364966]
  We aim to collect data related to engineering goals, challenges, and practices in start-up companies. We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start-ups.
  arXiv  Detail & Related papers  (2023-12-12T09:36:43Z)
• Software engineering in start-up companies: An analysis of 88 experience reports [3.944126365759018]
  This study investigates how software engineering is applied in start-up context. We identify the most frequently reported software engineering (requirements engineering, software design and quality) and business aspect (vision and strategy development) knowledge areas. We conclude that most engineering challenges in start-ups stem from inadequacies in requirements engineering.
  arXiv  Detail & Related papers  (2023-11-20T19:42:37Z)
• Software Startups -- A Research Agenda [14.364137253888037]
  This paper's research agenda focuses on software engineering in startups. It identifies, in particular, 70+ research questions in the areas of supporting startup engineering activities. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs.
  arXiv  Detail & Related papers  (2023-08-24T14:20:21Z)
• Software Engineering Knowledge Areas in Startup Companies: A Mapping Study [3.944126365759018]
  This study identifies and categorizes software engineering knowledge areas utilized in startups to map out the state-of-art. Previous research does not provide reliable support for software engineering in any phase of a startup life cycle.
  arXiv  Detail & Related papers  (2023-08-15T08:26:02Z)
• Using Machine Learning To Identify Software Weaknesses From Software Requirement Specifications [49.1574468325115]
  This research focuses on finding an efficient machine learning algorithm to identify software weaknesses from requirement specifications. Keywords extracted using latent semantic analysis help map the CWE categories to PROMISE_exp. Naive Bayes, support vector machine (SVM), decision trees, neural network, and convolutional neural network (CNN) algorithms were tested.
  arXiv  Detail & Related papers  (2023-08-10T13:19:10Z)
• Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
  We argue that ethical considerations need to be embedded into the (agile) software development process. We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
  arXiv  Detail & Related papers  (2021-07-15T11:14:03Z)
• Software engineering for artificial intelligence and machine learning software: A systematic literature review [6.681725960709127]
  This study aims to investigate how software engineering has been applied in the development of AI/ML systems. Main challenges faced by professionals are in areas of testing, AI software quality, and data management.
  arXiv  Detail & Related papers  (2020-11-07T11:06:28Z)
• Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
  The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
  arXiv  Detail & Related papers  (2020-05-27T11:56:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.