Trust and trustworthiness judgments have been studied in the context of social, business, and romantic relationships as well as between humans and automation. This article extends the prior research to explore how programmers assess code for trustworthiness when asked to reuse existing computer code. We used cognitive task analysis methods to explore experienced programmers’ first-person perspectives on code reuse. We elicited specific cues and strategies used to assess trustworthiness in real-world scenarios. Using qualitative analysis techniques, we grouped cues into three trustworthiness factors: performance, transparency, and reputation. We also identified environmental factors that influence acceptable levels of trust, including customer needs and requirements, organizational resources and constraints, and consequences of failure. We propose a descriptive model based on these findings. These findings have important implications for organizations that intend to reuse, adapt, and extend code over time. Writing code with the factors such as reputation, transparency, and performance in mind will increase the likelihood that it will be trusted in the near term and be reusable in the future. Furthermore, this research provides an important foundation for future studies exploring trusting behaviors, individual differences, and the ability to detect malicious code.

Computer programs (code) are integral to the functions of current society. Yet, little is known about why programmers trust code they did not create. The current paper applied the heuristic-systematic model (HSM) of information processing to perceptions of code trust for reuse. The studies explored transparency (readability and organization) and reputation (source) as factors that influenced trust perceptions and time spent reviewing code using professional programmers. Source and readability manipulations led to higher trustworthiness assessments in the first study. Organization had nonlinear effects on trustworthiness. A three-way interaction including time was also found. The second online study largely replicated the first study’s main and interaction effects for trustworthiness, but the main effects on time were not significant. Our findings suggest the relationships of transparency on trustworthiness are not as straightforward as previously thought. Additionally, the findings of the current study expand the HSM to trust in code.