Software Engineering Antipatterns in Start-Ups

Klotins, Eriks; Unterkalmsteiner, Michael; Gorschek, Tony

doi:10.1109/ms.2018.227105530

Cited by 11 publications

(20 citation statements)

References 16 publications

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“…The high failure rate could be explained by market challenges, team issues, difficulties in securing funding and so on. However, the capability to build software efficiently with limited understanding about stakeholder needs and with limited resources is the foremost challenge in software start-ups and precedes any market or business related challenges [23].…”

Section: Software Start-upsmentioning

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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Section: Software Start-upsmentioning

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.

Self Cite

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“…An unstable and difficult to maintain product adds risk to the company, for example, by limiting the ability to quickly enter into new markets (i.e. to pivot [38]) or to launch new innovative features [19,39]. That said, we are not advocating for the removal of all technical debt.…”

Section: Introductionmentioning

confidence: 99%

“…Our earlier study on software engineering anti-patterns in startups [19] indicates that poorly managed technical debt could be one contributing factor to high start-up failure rates, driven by poor product quality and difficult maintenance. Negative effects of technical debt on team productivity has also been observed [5].…”

Section: Introductionmentioning

confidence: 99%

Exploration of technical debt in start-ups

Klotins

Unterkalmsteiner

Chatzipetrou

et al. 2018

Proceedings of the 40th International Conference on Software Engineering: Software Engineering in Practice

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Context: Software start-ups are young companies aiming to build and market software-intensive products fast with little resources. Aiming to accelerate time-to-market, start-ups often opt for ad-hoc engineering practices, make shortcuts in product engineering, and accumulate technical debt. Objective: In this paper we explore to what extent precedents, dimensions and outcomes associated with technical debt are prevalent in start-ups. Method: We apply a case survey method to identify aspects of technical debt and contextual information characterizing the engineering context in start-ups. Results: By analyzing responses from 86 start-up cases we found that start-ups accumulate most technical debt in the testing dimension, despite attempts to automate testing. Furthermore, we found that start-up team size and experience is a leading precedent for accumulating technical debt: larger teams face more challenges in keeping the debt under control. Conclusions: This study highlights the necessity to monitor levels of technical debt and to preemptively introduce practices to keep the debt under control. Adding more people to an already difficult to maintain product could amplify other precedents, such as resource shortages, communication issues and negatively affect decisions pertaining to the use of good engineering practices.

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“…With no funding, they also found it difficult to experiment with hardware solutions. All in all, this resulted in an unreasonably long development cycle for an initial version of the product, which has been considered a key anti-pattern in software startups [4].…”

Section: Discussionmentioning

confidence: 99%

“…Various other characteristics have been attributed to startups in an attempt to differentiate them from other business organizations. These include the aforementioned lack of resources, as well as such attributes as (1) highly reactive, (2) innovation, (3) uncertainty, (4) rapidly evolving, (5) time-pressure, (6) third party dependency, (7) small team, (9) one product), (10) low-experienced team, (11) new company, (12) flat organization, (13) highly risky, (14) not self-sustained, and (15) little working history [15]. While these characteristics are seldom directly used to define what a software startup is, they are often listed in various contexts while discussing software startups are, in the absence of a widely accepted definition for a software startup.…”

Section: Introductionmentioning

confidence: 99%

Amidst Uncertainty – or Not? Decision-Making in Early-Stage Software Startups

Kemell

Ventilä

Kettunen

et al. 2019

Lecture Notes in Business Information Processing

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It is commonly claimed that the initial stages of any startup business are dominated by continuous, extended uncertainty, in an environment that has even been described as chaotic. Consequently, decisions are made in uncertain circumstances, so making the right decision is crucial to successful business. However, little currently exists in the way of empirical studies into this supposed uncertainty. In this paper, we study decision-making in early-stage software startups by means of a single, in-depth case study. Based on our data, we argue that software startups do not work in a chaotic environment, nor are they characterized by unique uncertainty unlike that experienced by other firms.

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Software Engineering Antipatterns in Start-Ups

Cited by 11 publications

References 16 publications

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

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

Exploration of technical debt in start-ups

Amidst Uncertainty – or Not? Decision-Making in Early-Stage Software Startups

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