What Needs Attention? Prioritizing Drivers of Developers’ Trust and Adoption of Generative AI
Rudrajit Choudhuri; Bianca Trinkenreich; Rahul Pandita; Eirini Kalliamvakou; Igor Steinmacher; Marco Aurélio Gerosa; Christopher A. Sanchez; Anita Sarma · 2026 · ACM Transactions on Software Engineering and Methodology
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Generative AI (genAI) tools promise productivity gains, yet developers still struggle to determine when to trust and effectively integrate these tools into their everyday work. Moreover, genAI can be exclusionary, failing to adequately support developers across individual differences. One such difference is cognitive style , which can shape how developers engage with genAI (e.g., risk-averse developers may gate outputs behind tests, whereas risk-tolerant ones may prototype directly and address issues post hoc). When tools fail to accommodate these differences, they can create additional usabil
Abstract by Rudrajit Choudhuri; Bianca Trinkenreich; Rahul Pandita; Eirini Kalliamvakou; Igor Steinmacher; Marco Aurélio Gerosa; Christopher A. Sanchez; Anita Sarma, ACM Transactions on Software Engineering and Methodology (2026) — licensed CC BY 4.0.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Estimating the reproducibility of psychological science
Negative / Null Result ReportWillingness to Share Research Data Is Related to the Strength of the Evidence and the Quality of Reporting of Statistical Results
Methods Dead-EndComparing multiple comparisons: practical guidance for choosing the best multiple comparisons test
Replication FailureInferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication
Negative / Null Result ReportMOBILE BANKING ADOPTION: APPLICATION OF DIFFUSION OF INNOVATION THEORY
Negative / Null Result ReportEnjoyment and social influence: predicting mobile payment adoption
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1145/3842739