Exploration of technical debt in start-ups
- URL: http://arxiv.org/abs/2309.12434v1
- Date: Thu, 21 Sep 2023 19:02:02 GMT
- Title: Exploration of technical debt in start-ups
- Authors: Eriks Klotins, Michael Unterkalmsteiner, Panagiota Chatzipetrou, Tony
Gorschek, Rafael Prikladnicki, Nirnaya Tripathi, Leandro Bento Pompermaier
- Abstract summary: We apply a case survey method to identify aspects of technical debt and contextual information characterizing the engineering context in start-ups.
We found that start-ups accumulate most technical debt in the testing dimension, despite attempts to automate testing.
We found that start-up team size and experience is a leading precedent for accumulating technical debt.
- Score: 5.664445343364966
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design.
For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent.
For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
arXiv Detail & Related papers (2024-07-01T16:53:00Z) - Technical Debt Management: The Road Ahead for Successful Software
Delivery [40.45645113369735]
Technical Debt, considered by many to be the'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers.
In this paper, we examine the state of the art in both industry and research communities in managing Technical Debt.
arXiv Detail & Related papers (2024-03-11T07:48:35Z) - Defining and executing temporal constraints for evaluating engineering
artifact compliance [56.08728135126139]
Process compliance focuses on ensuring that the actual engineering work is followed as closely as possible to the described engineering processes.
Checking these process constraints is still a daunting task that requires a lot of manual work and delivers feedback to engineers only late in the process.
We present an automated constraint checking approach that can incrementally check temporal constraints across inter-related engineering artifacts upon every artifact change.
arXiv Detail & Related papers (2023-12-20T13:26:31Z) - 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 Development in Startup Companies: The Greenfield Startup Model [4.881718571745022]
This study aims to improve understanding of the software development strategies employed by startups.
We packaged the results in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible.
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.
arXiv Detail & Related papers (2023-08-18T10:08:10Z) - Artificial Intelligence for Technical Debt Management in Software
Development [0.0]
Review of existing research on the use of AI powered tools for technical debt avoidance in software development.
Suggests that AI has the potential to significantly improve technical debt management in software development.
Offers practical guidance for software development teams seeking to leverage AI in their development processes.
arXiv Detail & Related papers (2023-06-16T21:59:22Z) - AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities
and Challenges [60.56413461109281]
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes.
We discuss in depth the key types of data emitted by IT Operations activities, the scale and challenges in analyzing them, and where they can be helpful.
We categorize the key AIOps tasks as - incident detection, failure prediction, root cause analysis and automated actions.
arXiv Detail & Related papers (2023-04-10T15:38:12Z) - A Survey on Efficient Training of Transformers [72.31868024970674]
This survey provides the first systematic overview of the efficient training of Transformers.
We analyze and compare methods that save computation and memory costs for intermediate tensors during training, together with techniques on hardware/algorithm co-design.
arXiv Detail & Related papers (2023-02-02T13:58:18Z) - Compliance Costs of AI Technology Commercialization: A Field Deployment
Perspective [1.637145148171519]
Many AI startups are not financially prepared to cope with a broad spectrum of regulatory requirements.
Complex and varying regulatory processes across the globe subtly give advantages to well-established and resourceful technology firms.
The continuation of this trend may phase out the majority of AI startups and lead to giant technology firms' monopolies of AI technologies.
arXiv Detail & Related papers (2023-01-31T07:22:12Z) - Towards Machine Learning for Placement and Routing in Chip Design: a
Methodological Overview [72.79089075263985]
Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows.
Machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors.
arXiv Detail & Related papers (2022-02-28T06:28:44Z) - Qlib: An AI-oriented Quantitative Investment Platform [86.8580406876954]
AI technologies have raised new challenges to the quantitative investment system.
Qlib aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment.
arXiv Detail & Related papers (2020-09-22T12:57:10Z)
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.