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Software Development in Startup Companies: The Greenfield Startup Model

Carmine Giardino, Nicolò Paternoster, Michael Unterkalmsteiner, Tony Gorschek, Pekka Abrahamsson

arXiv:2308.09438v1cs.SE

TL;DR

Software engineering research offers limited and fragmented evidence about how early-stage startups develop software under uncertainty, resource constraints, and time pressure. The paper uses Grounded Theory and 13 startup cases to construct the Greenfield Startup Model, finding that startups prioritize rapid release and later must restructure development to support growth. The model identifies trade-offs and research opportunities for startup-specific engineering practices.

  • Problem

    Research on software engineering in early-stage startups is limited and fragmented despite the importance and distinct constraints of this context.

  • Method

    The study uses a Grounded Theory approach based on interviews with practitioners from 13 startups to develop the Greenfield Startup Model.

  • Results

    Startups prioritize releasing products quickly to verify product-market fit and adjust their trajectory, while growth later requires restructuring development and controlling technical debt.

  • Takeaways & Limitations

    The GSM provides a common direction and vocabulary for developing and validating engineering practices adapted to startup contexts.

  • Takeaways & Limitations

    The study’s external validity is limited by interviewing subjects whose evidence reflects CEO and CTO perspectives on their organizations.

Abstract

from arXiv · show

Software startups are newly created companies with no operating history and oriented towards producing cutting-edge products. However, despite the increasing importance of startups in the economy, few scientific studies attempt to address software engineering issues, especially for early-stage startups. If anything, startups need engineering practices of the same level or better than those of larger companies, as their time and resources are more scarce, and one failed project can put them out of business. In this study we aim to improve understanding of the software development strategies employed by startups. We performed this state-of-practice investigation using a grounded theory approach. We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible. This strategy allows startups to verify product and market fit, and to adjust the product trajectory according to early collected user feedback. The need to shorten time-to-market, by speeding up the development through low-precision engineering activities, is counterbalanced by the need to restructure the product before targeting further growth. The resulting implications of the GSM outline challenges and gaps, pointing out opportunities for future research to develop and validate engineering practices in the startup context.

1 INTRODUCTION

Software startups operate under uncertainty, limited resources, and intense time pressure, making conventional software processes difficult to apply. This study investigates early-stage engineering strategies and develops the Greenfield Startup Model to explain them.

  • Startup context: Software startups are newly created, high-tech organizations seeking aggressive growth in scalable markets.They typically have little or no operating history and operate amid uncertainty.
  • Study aim: The study examines how practitioners structure, plan, and control software projects from idea conception to the first open beta release.It uses semi-structured interviews with CEOs and CTOs from 13 startups and iteratively adjusts the model to emerging evidence.
  • Startup context: Uncertainty, limited resources, and market pressure make software development and adaptation especially challenging for startups.Startups must respond to changing market demands while operating with constrained resources.
  • Research problem: Prescriptive software methodologies are difficult to follow in the startup context, motivating research into startup-specific engineering activities.The combination of startup characteristics creates a distinct software development context.
  • Contribution: The Greenfield Startup Model abstracts empirically grounded development strategies but is intended as a common direction and vocabulary rather than a set of prescribed best practices.The model is based on data from 13 cases and supports future research on startup software development.
  • Contribution: The paper contributes an empirical investigation, a rigorously developed explanatory model, and identified opportunities for future research.The contributions address early-stage startup engineering and challenges faced by startups.

2 BACKGROUND

Prior work describes startups as flexible, resource-constrained environments that favor reactive and low-precision engineering, while emphasizing unresolved questions about startup-specific practices. The background motivates studying how engineering can support rapid market entry without ignoring later process and quality needs.

  • Startup practices: Resource constraints and product focus lead startups to favor reactive, low-precision practices over repeatable and controlled processes.Attempts to introduce lightweight processes have also been reported to fail when teams cut engineering practices under pressure.
  • Startup practices: Time-to-market is a strategic objective in market-driven software development, where requirements are often shaped by the software market.This context differs from developing software customized for a particular client.
  • Open question: Prior work asks whether startup-specific engineering practices can accelerate time-to-market while meeting customer needs.The paper treats detailed state-of-practice knowledge as a prerequisite for addressing that question.
  • Research landscape: Startup software engineering research remains fragmented, with relatively few studies forming a consistent body of knowledge.Earlier reviews identified limited and dispersed evidence on software engineering practices in startups.
  • Existing approaches: Lean and Agile approaches support flexibility, rapid learning, customer relationships, and avoidance of unnecessary functionality under uncertainty.The literature presents these approaches as ways to respond to changing conditions and limited resources.
  • Open question: Improved practices may shorten time-to-market or improve target-market accuracy, but existing studies do not focus specifically on startups.This leaves the contribution of practices such as requirements engineering unresolved in the startup setting.
  • Startup practices: Startup workflows are optimized as trade-offs, with teams adopting development styles that support immediate needs rather than one fixed process.The background describes this tendency as a “Just do it” approach.
  • Startup practices: Rigid process models can conflict with startup dynamics, limited resources, flexibility, and the need for quick product delivery.Quality is often narrowed toward minimal suitable functionality, including usability and scalability concerns.

3 RESEARCH METHODOLOGY

The study uses Grounded Theory to investigate software development strategies in early-stage startups through iterative interviews, questionnaires, coding, and model formation. The sample covers startups characterized by limited resources, uncertainty, recent creation, and growth ambitions.

  • Research scope: The study investigates how practitioners engineer software development strategies, focusing on project structure, planning, and control through the first open beta release.These boundaries define the study’s central research focus.
  • Research questions: The research asks how startups structure and execute engineering activities and how they consider product quality attributes.These questions guide the investigation of startup software development practices.
  • Data collection: The researchers conducted 13 semi-structured interviews with 13 companies and integrated tailored follow-up questionnaires using a Grounded Theory methodology.The interviews and questionnaires supported extraction of the Greenfield Startup Model.
  • Method: Grounded Theory allowed theory to emerge from practitioner interviews while hypotheses and questions were adjusted as analysis progressed.The researchers formally analyzed relations among concepts to generate and validate the final theory.
  • Method: The study used Corbin and Strauss’ Grounded Theory approach because prior knowledge supported defining the research problem beforehand.This version emphasizes researchers’ theoretical sensitivity.
  • Analysis process: The evolutionary process iteratively incorporated data into a case-study database, formed theoretical categories, updated the emergent theory, and checked theoretical saturation.The methodology was organized into macro phases including data collection, analysis, and model generation.
  • Sampling: The sample was selected through initial convenience sampling followed by theoretical sampling of five additional startups during theory formation.Companies were characterized by recent creation, limited resources, uncertainty, and ambitions to grow.
  • Sample characteristics: All but one company were founded within three years, and all but one released their first product within six months of idea conception.The studied products included web, mobile, and desktop applications launched across six nations.

4 RESULTS: GREENFIELD STARTUP MODEL

The Greenfield Startup Model abstracts software development in early-stage startups into seven high-level categories centered on speed up development. Startups prioritize rapid delivery through lightweight engineering and flexible teams, while technical debt can hinder productivity as they grow.

  • 4 RESULTS: GREENFIELD STARTUP MODEL: The GSM comprises 128 sub-categories, 35 groups, and 7 high-level categories describing early-stage startup software development.The model is an abstraction of empirical data from thirteen startups.
  • 4 RESULTS: GREENFIELD STARTUP MODEL: Speed up development is the core category and the most interconnected node in the theory.Its centrality reflects the category’s greatest explanatory power.
  • 4 RESULTS: GREENFIELD STARTUP MODEL: Severe resource shortages constrain development capabilities and force startups to implement an essential set of functionalities, giving product quality lower priority.The model identifies limited human, time, and intellectual resources as central constraints, with some exceptions where quality matters.
  • 4 RESULTS: GREENFIELD STARTUP MODEL: Startups use flexible, evolutionary development and small, informal teams to release functioning but faulty products quickly.Low initial attention to architecture and lightweight processes support short time-to-market cycles.
  • 4 RESULTS: GREENFIELD STARTUP MODEL: The resulting technical debt may remain hidden while startups seek product/market fit but can later hinder productivity when users, products, and development teams grow.Growth requires startups to pay down accumulated debt.

4.2 Severe lack of resources

Startups face severe shortages of time, people, and expertise, shaping development around rapid delivery and broad individual responsibilities. Small, capable, self-organized teams compensate through multi-role work, tacit coordination, and external advice.

  • 4.2 Severe lack of resources: Severe lack of resources consists of time-shortage, limited human resources, and limited access to expertise.These constraints characterize the uncertainty of startup development strategies.
  • 4.2 Severe lack of resources: Time is the scarcest resource because startups face constant investor, business, and internal deadline pressure.Teams may sacrifice their interests to prioritize time-to-market.
  • 4.2 Severe lack of resources: Limited staffing leads startups to rely on multi-role and full-stack engineers who handle diverse development and business tasks.Developers may move across mobile, web, marketing, and sales responsibilities.
  • 4.2 Severe lack of resources: Small, co-located teams maintain speed and flexibility through high coordination, tacit knowledge, and informal discussion instead of extensive documentation.Practitioners reported that keeping the team small supports fast work.
  • 4.2 Severe lack of resources: Limited expertise makes teams depend on personal abilities and, where needed, mentors or advisors.Founders and technical leaders also strongly influence the development approach, while many decisions remain collective.

4.4 Evolutionary approach

Startups use evolutionary prototyping to validate product/market fit quickly, releasing a small set of functionalities and refining them through user feedback. This accelerates learning while keeping preliminary architecture and broader quality work limited.

  • 4.4 Evolutionary approach: Startups build an initial prototype and iteratively refine it to validate product/market fit through user feedback.They can release only the system parts needed for early validation.
  • 4.4 Evolutionary approach: Highly evolutionary approaches suit startups because uncertain conditions make long-term planning and unvalidated assumptions impractical.Flexibility and reactiveness are prioritized over fixed plans.
  • 4.4 Evolutionary approach: Functioning prototypes and small iterations provide fast user responses and progressively expand the first version.Startups begin with rough products, then polish, fix, and iterate.
  • 4.4 Evolutionary approach: Releasing a small number of good-enough functionalities helps startups avoid over-engineering and adjust development toward users’ needs.The approach limits investment in complex features not tested with real users.
  • 4.4 Evolutionary approach: Startups prioritize a limited number of suitable functionalities over non-functional requirements, enabling simpler products and less preliminary architectural study.User experience remains a prominent quality concern, especially under resource and time constraints.
  • 4.4 Evolutionary approach: Product metrics and user feedback help determine user experience, while efficiency and reliability can receive lower initial priority.Examples include A/B testing, usability feedback, and tolerance for unreliable beta products.

4.6 Speed-up development

Speeding up development is the GSM’s primary startup objective, supported by evolutionary work, informal processes, externalized complexity, and standards. These choices shorten delivery time but can defer quality work and create longer-term maintainability risks.

  • 4.6 Speed-up development: Speed up development is the GSM’s core category and the primary objective of early-stage startups.It represents the central characteristic of startup software development.
  • 4.6 Speed-up development: Startups combine evolutionary approaches, minimal suitable functionality, and simple informal workflows to remain flexible and reactive.Self-organized teams and broad developer responsibilities support this strategy.
  • 4.6 Speed-up development: Startups often treat development practices and systematic quality assurance as expendable, postponing efficiency and other quality concerns until after release.User experience receives more attention than several other quality attributes during initial delivery.
  • 4.6 Speed-up development: External services, commercial components, and open-source solutions reduce completion time and help products remain reasonably ready to scale.These approaches can introduce interoperability issues.
  • 4.6 Speed-up development: Standards and frameworks reduce the need for formal architectural design because they are documented, tested, and ready to use.Known technologies are selected to accelerate implementation.
  • 4.6 Speed-up development: Developer motivation can increase team performance, whereas missed deadlines reduce morale and hinder development speed.Motivation is linked to disruptive products, personal abilities, and market use.
  • 4.6 Speed-up development: Overtime may meet short-term deadlines but can produce poorly maintainable code and developer burnout over time.The paper presents this as an effective strategy only in the short term.

4.7 Accumulated technical debt

Early-stage startups accelerate delivery by minimizing formal engineering activities, but the resulting technical debt eventually forces restructuring and reduces development performance as the product and company grow.

  • Startups accept flawed code and technical debt to maintain a development speed that remains fast enough for early delivery.They aim to move quickly without making the codebase impossible to update.
  • Informal feature specifications, rough feasibility studies, and low-precision architectural designs replace traditional requirements and analysis activities.These choices support rapid implementation but can create costly early mistakes, including incorrect data structures.
  • Manual smoke testing and production feedback partly substitute for automated verification and validation during early development.Internal testing and early user reports are used to identify bugs after release.
  • Minimal project management and extensive verbal communication reduce coordination overhead but increase tacit knowledge and documentation gaps.Short informal milestones, low-precision task assignment, and lightweight progress tracking are typical in this phase.
  • As users, teams, and product complexity increase, startups introduce more structured management and must repay technical debt through refactoring and re-engineering.Growth brings greater quality and scalability demands, while poorly engineered code makes changes increasingly interrelated and difficult to manage.

4.9 Paradigm model

The GSM explains early-stage startup development as a speed-focused strategy for finding product/market fit under uncertainty and resource scarcity, with accumulated technical debt producing a temporary performance drop-off.

  • The model was generated from empirical data using grounded-theory procedures and organized into categories, subcategories, and their relationships.The theory was developed bottom-up through coding and formal model construction.
  • Early-stage startups focus on suitable functionalities and rapid evolutionary development to find product/market fit quickly.Skilled, highly co-located developers support this high-speed approach.
  • The GSM identifies low product-quality priority, evolutionary development, and the team as causal conditions for speeding development.The core phenomenon is explicitly defined as speed up development.
  • The model is bounded to early-stage web startups with severe resource constraints, uncertain environments, and an aim to find product/market fit early.Accumulated technical debt represents the principal action and interaction strategy within this context.
  • Accumulated technical debt is associated with a temporary performance drop-off as an outcome of the speed-focused strategy.

5 IMPLICATIONS OF THE GSM

The GSM implies that startups should prioritize rapid product/market learning with lightweight engineering, then introduce structure and scalable practices as complexity grows, while research adapts support to startup constraints.

  • Startup development strategy: Shortening time-to-market to find product/market fit is the most urgent startup development priority.Startups often release a first version without adopting a standard development methodology.
  • Startup development strategy: Early-stage startups use fast “build and fix” cycles because uncertainty makes extensive analysis and immediate application of best practices potentially counter-productive.They develop a limited set of suitable functionalities to obtain an early market response.
  • Research and practice implications: Technology-transfer models for startups require adaptation because existing state-of-the-art models assume long-term participant commitment.This is identified as an opportunity for future research.
  • Lightweight support: Lightweight empowerment, requirements techniques, coding platforms, and frameworks can support speed, but each addresses only selected organizational or technical needs.Persona and Scenario techniques can improve elicitation, while coding platforms integrate several engineering activities and frameworks reduce overhead.
  • Technical debt: Technical debt may be treated as an investment, but technology-selection approaches and third-party components must be adapted to early-stage startup constraints.Some failed features or products may effectively result in written-off debt.
  • Research and practice implications: Startups should progressively integrate scalable solutions, team empowerment, minimal project management, and longer-term Agile or Lean practices after initial chaos is managed.The proposed sequence balances fast iteration with later planning and performance management.

6 THEORY VALIDATION

The GSM is compared with prior startup and software-development models and literature mappings, while validation identifies uncovered confounding factors that constrain interpretation and generalizability.

  • Comparison with other models: The GSM categories are mapped to concepts from related models to assess whether prior work supports its conceptualizations.The comparison includes models by Coleman, Baskerville, and Brooks.
  • Comparison with other models: Related studies support the GSM’s emphasis on minimum process, rapid development, informal communication, and founder influence on development strategies.Coleman’s findings align with the GSM’s account of process cost, process erosion, and CEO/CTO influence.
  • Comparison with other models: Brooks’s mitigation strategies, including requirements refinement, rapid prototyping, incremental development, and strong teams, fit the GSM’s startup practices.
  • Literature overlap: Seven of 37 reviewed studies address all GSM categories, while 29 address speed-up development and 26 address the team as development catalyst.
  • Confounding factors: The GSM does not cover creativity and innovation, market requirements and application type, or developer experience as potential confounding factors.These variables may influence the model positively or negatively and must be considered when applying it.

7 THREATS TO VALIDITY

The study identifies threats to the GSM’s validity involving participant selection, startup coverage, confounding factors, and qualitative research procedures. These limitations constrain how broadly the model’s relationships and findings can be generalized.

  • External validity: The study relied on CEO and CTO interviews, so its findings reflect only those organizational perspectives.The authors identify subject selection as an external-validity threat and note that no other data were considered.
  • External validity: Most studied startups were successful web companies, while failed startups and other technical domains might have produced different GSM results.The authors specifically identify the missing perspective of failed startups and companies such as embedded real-time systems.
  • Validity safeguards: The study used Grounded Theory, systematic coding, theoretical sampling, saturation, and comparisons with literature and prior models to strengthen validity.The process also included interviews, follow-up questionnaires, supporting artifacts, peer review, and comparative analysis, although some supporting artifacts were unavailable.
  • Internal validity: Interviewees were not told the study’s goals or emergent results, reducing the risk that awareness of the theory influenced their responses.The authors identify interviewee awareness of emergent theories as a separate threat and describe nondisclosure as its mitigation.

8 CONCLUSION

The study explains early-stage startup development through the Greenfield Startup Model, emphasizing rapid release, empirical feedback, and the eventual need to control growth-related complexity. It identifies four objectives for improving startup engineering while highlighting the trade-off between speed and technical debt.

  • Study contribution: The study uses a Grounded Theory approach based on 13 cases to explain early-stage startup software development from idea conception to the first open beta release.The model provides an empirical explanation of startup development strategies and practices.
  • Core development strategy: Startups prioritize releasing products quickly to verify product/market fit and adjust business and product trajectories using early feedback and metrics.They often discard formal project management, documentation, analysis, planning, and testing in favor of evolutionary prototyping and externalized complexity.
  • Growth transition: As startups grow, increasing customers, employees, and product functionality create a need to restructure the product and control engineering activities.Growth counterbalances the initial gains in flexibility and speed.
  • Implications: The central challenge is finding a balance between entering the market quickly and controlling accumulated technical debt.The paper presents this balance as the most significant challenge for early-stage startups.
  • Implications: The GSM identifies four objectives: scalable solutions with fast iterations, empowered autonomous teams, minimal project management, and Lean and Agile practices after the initial chaotic phase.These objectives connect rapid early development with later process improvement.
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