Managing Human-Centric Software Defects: Insights from GitHub and Practitioners' Perspectives

• URL: http://arxiv.org/abs/2408.01621v1
• Date: Sat, 3 Aug 2024 01:08:38 GMT
• Title: Managing Human-Centric Software Defects: Insights from GitHub and Practitioners' Perspectives
• Authors: Vedant Chauhan, Chetan Arora, Hourieh Khalajzadeh, John Grundy,
• Abstract summary: Human-centric defects (HCDs) are nuanced and subjective defects that often occur due to end-user perceptions or differences. Development teams have a limited understanding of these issues, which leads to the neglect of these defects. Defect reporting tools do not adequately handle the capture and fixing of HCDs.
• Score: 8.285109854002307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Human-centric defects (HCDs) are nuanced and subjective defects that often occur due to end-user perceptions or differences, such as their genders, ages, cultures, languages, disabilities, socioeconomic status, and educational backgrounds. Development teams have a limited understanding of these issues, which leads to the neglect of these defects. Defect reporting tools do not adequately handle the capture and fixing of HCDs. Objective: This research aims to understand the current defect reporting process and tools for managing defects. Our study aims to capture process flaws and create a preliminary defect categorisation and practices of a defect-reporting tool that can improve the reporting and fixing of HCDs in software engineering. Method: We first manually classified 1,100 open-source issues from the GitHub defect reporting tool to identify human-centric defects and to understand the categories of such reported defects. We then interviewed software engineering practitioners to elicit feedback on our findings from the GitHub defects analysis and gauge their knowledge and experience of the defect-reporting process and tools for managing human-centric defects. Results: We identified 176 HCDs from 1,100 open-source issues across six domains: IT-Healthcare, IT-Web, IT-Spatial, IT-Manufacturing, IT-Finance, and IT-Gaming. Additionally, we interviewed 15 software practitioners to identify shortcomings in the current defect reporting process and determine practices for addressing these weaknesses. Conclusion: HCDs present in open-source repositories are fairly technical, and due to the lack of awareness and improper defect reports, they present a major challenge to software practitioners. However, the management of HCDs can be enhanced by implementing the practices for an ideal defect reporting tool developed as part of this study.

Related papers

• Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points [51.40935517552926]
  We introduce Focused-DPO, a framework that enhances code generation by directing preference optimization towards critical error-prone areas. By focusing on error-prone points, Focused-DPO advances the accuracy and functionality of model-generated code.
  arXiv  Detail & Related papers  (2025-02-17T06:16:02Z)
• Closing the Gap: A User Study on the Real-world Usefulness of AI-powered Vulnerability Detection & Repair in the IDE [5.824774194964031]
  We implement a vulnerability detection and fix tool with professional software developers on real projects that they own. DeepVulGuard scans code for vulnerabilities, suggests fixes, provides natural-language explanations for alerts and fixes, leveraging chat interfaces. We find that although state-of-the-art AI-powered detection and fix tools show promise, they are not yet practical for real-world use due to a high rate of false positives and non-applicable fixes.
  arXiv  Detail & Related papers  (2024-12-18T20:19:56Z)
• Definition and Detection of Centralization Defects in Smart Contracts [30.24160537607527]
  Security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. This paper introduces six types of centralization defects in smart contracts by manually analyzing 597 Stack Exchange posts and 117 audit reports. We introduce a tool named CDRipper (Centralization Defects Ripper) designed to identify the defined centralization defects.
  arXiv  Detail & Related papers  (2024-11-15T13:16:16Z)
• 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)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• 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)
• InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification [60.10193972862099]
  This work proposes a framework to characterize and recover simplification-induced information loss in form of question-and-answer pairs. QA pairs are designed to help readers deepen their knowledge of a text.
  arXiv  Detail & Related papers  (2024-01-29T19:00:01Z)
• Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Models [53.04357141450459]
  dcc --help was deployed to our CS1 and CS2 courses, with 2,565 students using the tool over 64,000 times in ten weeks. We found that the LLM-generated explanations were conceptually accurate in 90% of compile-time and 75% of run-time cases, but often disregarded the instruction not to provide solutions in code.
  arXiv  Detail & Related papers  (2023-08-23T02:36:19Z)
• Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities [7.2944322548786715]
  Security defects are not prevalently discussed in code review. More than half of the reviewers provided explicit fixing strategies/solutions to help developers fix security defects. Disagreement between the developer and the reviewer are the main causes for not resolving security defects.
  arXiv  Detail & Related papers  (2023-07-05T14:30:41Z)
• 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)
• HEDP: A Method for Early Forecasting Software Defects based on Human Error Mechanisms [1.0660480034605238]
  The main process behind a software defect is that an error-prone scenario triggers human error modes. The proposed idea emphasizes predicting the exact location and form of a possible defect.
  arXiv  Detail & Related papers  (2021-10-13T14:44:23Z)

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.