Effective Technical Reviews

• URL: http://arxiv.org/abs/2407.02355v1
• Date: Tue, 2 Jul 2024 15:19:52 GMT
• Title: Effective Technical Reviews
• Authors: Scott Ballentine, Eitan Farchi,
• Abstract summary: While executing a program is the ultimate test for its correctness reviewing the program can occur earlier in its development and find problems if done effectively. This work focuses on review techniques. It enables the programmer to effectively review a program and find a range of problems from to interface issues.
• Score: 0.7212939068975619
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are two ways to check if a program is correct, namely execute it or review it. While executing a program is the ultimate test for its correctness reviewing the program can occur earlier in its development and find problems if done effectively. This work focuses on review techniques. It enables the programmer to effectively review a program and find a range of problems from concurrency to interface issues. The review techniques can be applied in a time constrained industrial development context and are enhanced by knowledge on programming pitfalls.

Related papers

• Simulated Interactive Debugging [3.3333163429719677]
  We present our approach called Simulated Interactive that interactively guides students along the debug process. The guidance aims to empower the students to repair their solutions and have a proper "learning" experience.
  arXiv  Detail & Related papers  (2025-01-16T17:47:18Z)
• Go-Oracle: Automated Test Oracle for Go Concurrency Bugs [6.773048267569272]
  Bugs have become a prevalent issue within the Go programming language. Our work seeks to address the test oracle problem for Go programs, to automatically classify test executions as pass or fail. We capture a comprehensive array of execution events using the native Go execution tracer. We preprocess and encode these traces before training a transformer-based neural network to effectively classify the traces as either passing or failing.
  arXiv  Detail & Related papers  (2024-12-11T03:07:56Z)
• VDebugger: Harnessing Execution Feedback for Debugging Visual Programs [103.61860743476933]
  We introduce V Debugger, a critic-refiner framework trained to localize and debug visual programs by tracking execution step by step. V Debugger identifies and corrects program errors leveraging detailed execution feedback, improving interpretability and accuracy. Evaluations on six datasets demonstrate V Debugger's effectiveness, showing performance improvements of up to 3.2% in downstream task accuracy.
  arXiv  Detail & Related papers  (2024-06-19T11:09:16Z)
• NExT: Teaching Large Language Models to Reason about Code Execution [50.93581376646064]
  Large language models (LLMs) of code are typically trained on the surface textual form of programs. We propose NExT, a method to teach LLMs to inspect the execution traces of programs and reason about their run-time behavior.
  arXiv  Detail & Related papers  (2024-04-23T01:46:32Z)
• Code Review Automation: Strengths and Weaknesses of the State of the Art [14.313783664862923]
  Three code review automation techniques tend to succeed or fail in two tasks described in this paper. The study has a strong qualitative focus, with 105 man-hours of manual inspection invested in analyzing correct and wrong predictions.
  arXiv  Detail & Related papers  (2024-01-10T13:00:18Z)
• Is ChatGPT the Ultimate Programming Assistant -- How far is it? [11.943927095071105]
  ChatGPT has received great attention: it can be used as a bot for discussing source code. We present an empirical study of ChatGPT's potential as a fully automated programming assistant.
  arXiv  Detail & Related papers  (2023-04-24T09:20:13Z)
• Generation Probabilities Are Not Enough: Uncertainty Highlighting in AI Code Completions [54.55334589363247]
  We study whether conveying information about uncertainty enables programmers to more quickly and accurately produce code. We find that highlighting tokens with the highest predicted likelihood of being edited leads to faster task completion and more targeted edits.
  arXiv  Detail & Related papers  (2023-02-14T18:43:34Z)
• Giving Feedback on Interactive Student Programs with Meta-Exploration [74.5597783609281]
  Developing interactive software, such as websites or games, is a particularly engaging way to learn computer science. Standard approaches require instructors to manually grade student-implemented interactive programs. Online platforms that serve millions, like Code.org, are unable to provide any feedback on assignments for implementing interactive programs.
  arXiv  Detail & Related papers  (2022-11-16T10:00:23Z)
• Predicting Code Review Completion Time in Modern Code Review [12.696276129130332]
  Modern Code Review (MCR) is being adopted in both open source and commercial projects as a common practice. Code reviews can experience significant delays to be completed due to various socio-technical factors. There is a lack of tool support to help developers estimating the time required to complete a code review.
  arXiv  Detail & Related papers  (2021-09-30T14:00:56Z)
• IReEn: Reverse-Engineering of Black-Box Functions via Iterative Neural Program Synthesis [70.61283188380689]
  We investigate the problem of revealing the functionality of a black-box agent. We do not rely on privileged information on the black box, but rather investigate the problem under a weaker assumption of having only access to inputs and outputs of the program. Our results show that the proposed approach outperforms the state-of-the-art on this challenge by finding an approximately functional equivalent program in 78% of cases.
  arXiv  Detail & Related papers  (2020-06-18T17:50:48Z)
• Time-varying Gaussian Process Bandit Optimization with Non-constant Evaluation Time [93.6788993843846]
  We propose a novel time-varying Bayesian optimization algorithm that can effectively handle the non-constant evaluation time. Our bound elucidates that a pattern of the evaluation time sequence can hugely affect the difficulty of the problem.
  arXiv  Detail & Related papers  (2020-03-10T13:28:33Z)

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.