How Developers Choose Debugging Strategies for Challenging Web Application Defects

• URL: http://arxiv.org/abs/2501.11792v1
• Date: Mon, 20 Jan 2025 23:43:36 GMT
• Title: How Developers Choose Debugging Strategies for Challenging Web Application Defects
• Authors: Maryam Arab, Jenny T. Liang, Valentina Hong, Thomas D. LaToza,
• Abstract summary: This study investigates the factors influencing strategy choice in complex scenarios. We found that contextual factors interact in complex ways, and combinations of factors influence strategy choice. Our results show a gap between learning and effectively practicing strategies in challenging contexts.
• Score: 9.00716644826864
• License:
• Abstract: Effective debugging is a crucial aspect of software development, demanding problem-solving skills, expertise, and appropriate tools. Although previous research has studied expert developers' debugging strategies, the specific factors influencing strategy choice in complex scenarios remain underexplored. To investigate these contextual factors, we conducted two studies. First, we surveyed 35 developers to identify experiences with challenging debugging problems and contextual complexities. Second, we held semi-structured interviews with 16 experienced developers to gain deeper insight into strategic reasoning for complex debugging tasks. Insights from both groups enriched our understanding of debugging strategies at different expertise levels. We found that contextual factors interact in complex ways, and combinations of factors influence strategy choice, evolving throughout the debugging process. Hypothesis making is the baseline for debugging, with experience and code familiarity crucial for strategy selection. Our results show a gap between learning and effectively practicing strategies in challenging contexts, highlighting the need for carefully designed debugging tools and educational frameworks that align with problem contexts.

Related papers

• Defining Requirements Strategies in Agile: A Design Science Research Study [4.110602799032192]
  Research shows that many of the challenges currently encountered with agile development are related to requirements engineering. This paper investigates critical challenges that arise in agile development from an undefined requirements strategy.
  arXiv  Detail & Related papers  (2024-05-29T07:57:32Z)
• K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning [76.3114831562989]
  It requires Large Language Model (LLM) agents to adapt their strategies dynamically in multi-agent environments. We propose a novel framework: "K-Level Reasoning with Large Language Models (K-R)"
  arXiv  Detail & Related papers  (2024-02-02T16:07:05Z)
• StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving [76.5322280307861]
  StrategyLLM allows LLMs to perform inductive reasoning, deriving general strategies from specific task instances, and deductive reasoning, applying these general strategies to particular task examples, for constructing generalizable and consistent few-shot prompts. Experimental results demonstrate that StrategyLLM outperforms the competitive baseline CoT-SC that requires human-annotated solutions on 13 datasets across 4 challenging tasks without human involvement, including math reasoning (34.2% $rightarrow$ 38.8%), commonsense reasoning (70.3% $rightarrow$ 72.5%), algorithmic reasoning (73.7% $rightarrow$ 85.0
  arXiv  Detail & Related papers  (2023-11-15T09:18:09Z)
• Microservice API Evolution in Practice: A Study on Strategies and Challenges [45.085830389820956]
  loose coupling poses new challenges to the API evolution process. We conducted 17 semi-structured interviews with developers, architects, and managers in 11 companies. We identified six strategies and six challenges for REpresentational State Transfer (REST) and event-driven communication via message brokers.
  arXiv  Detail & Related papers  (2023-11-14T14:04:17Z)
• A Comprehensive Evaluation of Tool-Assisted Generation Strategies [39.30954697422296]
  A growing area of research investigates augmenting language models with tools to overcome their shortcomings. Various few-shot tool-usage strategies have been proposed, but there is no systematic and fair comparison. Our findings suggest that few-shot tool integration is still an open challenge.
  arXiv  Detail & Related papers  (2023-10-16T04:53:22Z)
• Risk-reducing design and operations toolkit: 90 strategies for managing risk and uncertainty in decision problems [65.268245109828]
  This paper develops a catalog of such strategies and develops a framework for them. It argues that they provide an efficient response to decision problems that are seemingly intractable due to high uncertainty. It then proposes a framework to incorporate them into decision theory using multi-objective optimization.
  arXiv  Detail & Related papers  (2023-09-06T16:14:32Z)
• On solving decision and risk management problems subject to uncertainty [91.3755431537592]
  Uncertainty is a pervasive challenge in decision and risk management. This paper develops a systematic understanding of such strategies, determine their range of application, and develop a framework to better employ them.
  arXiv  Detail & Related papers  (2023-01-18T19:16:23Z)
• Are Current Decoding Strategies Capable of Facing the Challenges of Visual Dialogue? [3.491999371287298]
  We compare different decoding strategies in a Visual Dialogue referential game. None of them successfully balance lexical richness, accuracy in the task, and visual grounding. We believe our findings and suggestions may serve as a starting point for designing more effective decoding algorithms.
  arXiv  Detail & Related papers  (2022-10-24T07:34:39Z)
• Investigating the Impact of Backward Strategy Learning in a Logic Tutor: Aiding Subgoal Learning towards Improved Problem Solving [6.639504127104268]
  The training session involved backward worked examples (BWE) and problem-solving (BPS) to help students learn backward strategy. Our results showed that, when new problems were given to solve without any tutor help, students who were trained with both BWE and BPS outperformed students who received none of the treatment or only BWE during training.
  arXiv  Detail & Related papers  (2022-07-27T00:43:52Z)
• AR-LSAT: Investigating Analytical Reasoning of Text [57.1542673852013]
  We study the challenge of analytical reasoning of text and introduce a new dataset consisting of questions from the Law School Admission Test from 1991 to 2016. We analyze what knowledge understanding and reasoning abilities are required to do well on this task.
  arXiv  Detail & Related papers  (2021-04-14T02:53:32Z)

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.