Towards Effective Technical Debt Decision Making in Software Startups: A Multiple Case Study of Web and Mobile App Startups
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Saudi Digital Library
Abstract
Technical Debt (TD) is a suboptimal technical solution to expedite and reduce the
cost of the software development in the short term, but entails extra work in future. It
has been used intensively in startup organizations to cope with the limited resources
and the uncertainty about the product-market fit. Despite the many advantages that
TD can provide for startups (e.g., faster release within minimum cost), it could have
some negative impacts that directly affect the startup team’s ability to maintain and
evolve the software. These impacts could also escalate to affect the velocity of
delivering software releases and the ability to meet market demand. Currently, there
is no clear guidance about how startups can effectively deal with TD. In this
dissertation, we aim to help software team in startups to maximize the benefits of TD
while minimizing its negative impacts.
We conducted a multiple case study to investigate TD decisions in five
web/mobile app startups, to understand how TD decisions are made, their impacts,
and how they should have been made in hindsight. Our study covered different
timeframes within the startup evolution (i.e., starting from when a startup is founded
until it becomes a mature organization). For each case, we interviewed the CEO/CTO
and several software developers (a total of 17 participants) and analyzed public
documents. First, we focused on four cases to generate our results and develop our
decision model. Then, we conducted the fifth case to evaluate and refine our decision
model. Finally, we performed three follow-up interviews with three participants from
different cases to evaluate our final decision model. Our results provide an easy-to-interpret
decision model that can guide software teams in startups to make effective
TD decisions throughout the startup evolution.