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

• URL: http://arxiv.org/abs/2410.20696v2
• Date: Sun, 09 Feb 2025 18:21:55 GMT
• Title: "So Am I Dr. Frankenstein? Or Were You a Monster the Whole Time?": Mitigating Software Project Failure With Loss-Aversion-Aware Development Methodologies
• Authors: Junade Ali,
• Abstract summary: We conduct a study of the experiences of 600 software engineers in the UK and USA on project success experiences.<n> 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.
• Score: 0.3626013617212666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Measuring Agents in Production [133.77818981073457]
  We present the first large-scale systematic study of AI agents in production.<n>We find that production agents are typically built using simple, controllable approaches.<n> Reliability remains the top development challenge, driven by difficulties in ensuring and evaluating agent correctness.
  arXiv  Detail & Related papers  (2025-12-02T16:45:10Z)
• QueST: Incentivizing LLMs to Generate Difficult Problems [77.75835742350644]
  Large Language Models have achieved strong performance on reasoning tasks, solving competition-level coding and math problems.<n>Existing competitive coding datasets contain only thousands to tens of thousands of problems.<n>We propose QueST, a novel framework which combines difficulty-aware graph sampling and difficulty-aware rejection fine-tuning.
  arXiv  Detail & Related papers  (2025-10-20T16:29:53Z)
• Learning From Software Failures: A Case Study at a National Space Research Center [38.518223399280835]
  We conduct a case study through 10 in-depth interviews with research software engineers at a national space research center.<n>We examine how they learn from failures: how they gather, document, share, and apply lessons.<n>Our findings provide insight into how engineers learn from failures in practice.
  arXiv  Detail & Related papers  (2025-09-08T03:02:53Z)
• I Felt Pressured to Give 100% All the Time: How Are Neurodivergent Professionals Being Included in Software Development Teams? [0.46873264197900916]
  This study seeks to understand the work experiences of neurodivergent professionals acting in different software development roles. We applied the Sociotechnical Theory (STS) to investigate how the social structures of organizations and their respective work technologies influence the inclusion of these professionals.
  arXiv  Detail & Related papers  (2025-03-12T02:28:59Z)
• Seeker: Towards Exception Safety Code Generation with Intermediate Language Agents Framework [58.36391985790157]
  In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code. We explore the use of large language models (LLMs) to improve exception handling in code. We propose Seeker, a multi-agent framework inspired by expert developer strategies for exception handling.
  arXiv  Detail & Related papers  (2024-12-16T12:35:29Z)
• Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement [62.94719119451089]
  Lingma SWE-GPT series learns from and simulating real-world code submission activities. Lingma SWE-GPT 72B resolves 30.20% of GitHub issues, marking a significant improvement in automatic issue resolution.
  arXiv  Detail & Related papers  (2024-11-01T14:27:16Z)
• Causal Reasoning in Software Quality Assurance: A Systematic Review [11.887059800587672]
  This study provides a systematic review of the scientific literature on causal reasoning for SQA. Fault localization is the activity where causal reasoning is more exploited, especially in the web services/microservices domain. tools to favour their application are appearing at a fast pace - most of them after 2021.
  arXiv  Detail & Related papers  (2024-08-30T10:34:11Z)
• Leveraging Large Language Models for Efficient Failure Analysis in Game Development [47.618236610219554]
  This paper proposes a new approach to automatically identify which change in the code caused a test to fail. The method leverages Large Language Models (LLMs) to associate error messages with the corresponding code changes causing the failure. Our approach reaches an accuracy of 71% in our newly created dataset, which comprises issues reported by developers at EA over a period of one year.
  arXiv  Detail & Related papers  (2024-06-11T09:21:50Z)
• Learning From Lessons Learned: Preliminary Findings From a Study of Learning From Failure [3.045851438458641]
  Organizations analyze and learn from system failures. Co-evolve both the technical and human parts of their systems based on what they learn. Despite established processes and tool support, it is not straightforward to take what was learned from a failure and successfully improve the reliability of the socio-technical system.
  arXiv  Detail & Related papers  (2024-02-14T19:29:04Z)
• How do software practitioners perceive human-centric defects? [9.05088731726381]
  Human-centric software design focuses on how users want to carry out their tasks rather than making users accommodate their software. There is a lack of awareness regarding human-centric aspects, causing them to be lost or under-appreciated during software development.
  arXiv  Detail & Related papers  (2024-02-05T04:55:15Z)
• Competition-Level Problems are Effective LLM Evaluators [121.15880285283116]
  This paper aims to evaluate the reasoning capacities of large language models (LLMs) in solving recent programming problems in Codeforces. We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered. Surprisingly, theThoughtived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems.
  arXiv  Detail & Related papers  (2023-12-04T18:58:57Z)
• Embedded Software Development with Digital Twins: Specific Requirements for Small and Medium-Sized Enterprises [55.57032418885258]
  Digital twins have the potential for cost-effective software development and maintenance strategies. We interviewed SMEs about their current development processes. First results show that real-time requirements prevent, to date, a Software-in-the-Loop development approach.
  arXiv  Detail & Related papers  (2023-09-17T08:56:36Z)
• EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery [54.34661595290837]
  We propose an efficient and accurate pre-trained framework called EALink for issue-commit link recovery. We construct a large-scale dataset and conduct extensive experiments to demonstrate the power of EALink. Results show that EALink outperforms the state-of-the-art methods by a large margin (15.23%-408.65%) on various evaluation metrics.
  arXiv  Detail & Related papers  (2023-08-21T14:46:43Z)
• Comparing Software Developers with ChatGPT: An Empirical Investigation [0.0]
  This paper conducts an empirical investigation, contrasting the performance of software engineers and AI systems, like ChatGPT, across different evaluation metrics. The paper posits that a comprehensive comparison of software engineers and AI-based solutions, considering various evaluation criteria, is pivotal in fostering human-machine collaboration.
  arXiv  Detail & Related papers  (2023-05-19T17:25:54Z)
• Why is the winner the best? [78.74409216961632]
  We performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE I SBI 2021 and MICCAI 2021. Winning solutions typically include the use of multi-task learning (63%), and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%) Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases.
  arXiv  Detail & Related papers  (2023-03-30T21:41:42Z)
• SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video Games Using Risk Based Testing and Machine Learning [62.997667081978825]
  Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems. We present SUPERNOVA, a system responsible for test selection and defect prevention while also functioning as an automation hub. The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title.
  arXiv  Detail & Related papers  (2022-03-10T00:47:46Z)
• Machine Learning Techniques for Software Quality Assurance: A Survey [5.33024001730262]
  We discuss various approaches in both fault prediction and test case prioritization. Recent studies deep learning algorithms for fault prediction help to bridge the gap between programs' semantics and fault prediction features.
  arXiv  Detail & Related papers  (2021-04-29T00:37:27Z)
• The Unpopularity of the Software Tester Role among Software Practitioners: A Case Study [10.028628621669293]
  This work attempts to understand the motivation/de-motivation of software practitioners to take up and sustain testing careers. One hundred and forty four software practitioners from several Cuban software insti-tutes were surveyed. Individuals were asked the PROs (advantages or motiva-tors) and CONs (disadvantages or de-motivators) of taking up a career in soft-ware testing and their chances of doing so.
  arXiv  Detail & Related papers  (2020-07-16T14:52:36Z)

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.