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"So Am I Dr. Frankenstein? Or Were You a Monster the Whole Time?": Mitigating Software Project Failure With Loss-Aversion-Aware Development Methodologies

Junade Ali

TL;DR

The paper tackles software project failure by treating loss aversion and psychological safety as core drivers. It introduces Impact Engineering (IE), a loss-aversion-aware methodology, and evaluates it through a large-scale UK/USA survey of 600 software engineers, linking upfront clear requirements and the ability to discuss problems to substantially higher success rates. The results show that upfront requirements yield a 97% improvement in project success, and psychological safety yields an 87% improvement, with IE reducing failure rates by about 54% relative to baseline; cross-country differences in practice prevalence are also observed. The study argues for integrating human-factor considerations—especially upfront requirements gating—into software development practices and acknowledges the need for randomized trials and broader stakeholder perspectives in future work.

Abstract

Case studies have shown that software disasters snowball from technical issues to catastrophes through humans covering up problems rather than addressing them and empirical research has found the psychological safety of software engineers to discuss and address problems to be foundational to improving project success. However, the failure to do so can be attributed to psychological factors like loss aversion. We conduct a large-scale study of the experiences of 600 software engineers in the UK and USA on project success experiences. Empirical evaluation finds that approaches like ensuring clear requirements before the start of development, when loss aversion is at its lowest, correlated to 97% higher project success. The freedom of software engineers to discuss and address problems correlates with 87% higher success rates. The findings support the development of software development methodologies with a greater focus on human factors in preventing failure.

"So Am I Dr. Frankenstein? Or Were You a Monster the Whole Time?": Mitigating Software Project Failure With Loss-Aversion-Aware Development Methodologies

TL;DR

The paper tackles software project failure by treating loss aversion and psychological safety as core drivers. It introduces Impact Engineering (IE), a loss-aversion-aware methodology, and evaluates it through a large-scale UK/USA survey of 600 software engineers, linking upfront clear requirements and the ability to discuss problems to substantially higher success rates. The results show that upfront requirements yield a 97% improvement in project success, and psychological safety yields an 87% improvement, with IE reducing failure rates by about 54% relative to baseline; cross-country differences in practice prevalence are also observed. The study argues for integrating human-factor considerations—especially upfront requirements gating—into software development practices and acknowledges the need for randomized trials and broader stakeholder perspectives in future work.

Abstract

Case studies have shown that software disasters snowball from technical issues to catastrophes through humans covering up problems rather than addressing them and empirical research has found the psychological safety of software engineers to discuss and address problems to be foundational to improving project success. However, the failure to do so can be attributed to psychological factors like loss aversion. We conduct a large-scale study of the experiences of 600 software engineers in the UK and USA on project success experiences. Empirical evaluation finds that approaches like ensuring clear requirements before the start of development, when loss aversion is at its lowest, correlated to 97% higher project success. The freedom of software engineers to discuss and address problems correlates with 87% higher success rates. The findings support the development of software development methodologies with a greater focus on human factors in preventing failure.

Paper Structure

This paper contains 8 sections, 6 tables.