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Applying Software Engineering Anti-patterns to programming error messages 2023

Applying Software Engineering Anti-patterns to Programming E...

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54th Annual ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE TS)

作者： Santos, Eddie Antonio Karvelas, Ioannis Becker, Brett A. Univ Coll Dublin Dublin Ireland

ISBN: (纸本)9781450394338

programming error messages (PEMs) have long been a hindrance to novice programmers. This work aims to establish a catalog of PEM anti-patterns-common, reoccurring features of PEMs that make them unhelpful or actively harmful to programmers. The goal is for educators to be aware of, and actively teach concrete ways that PEMs can be misleading to students;to encourage language implementers to be cognizant of these;and avoid them when designing error feedback. A pilot study is being conducted to validate the presence of anti-patterns in error messages.

关键词： human factors programming error messages computer science education error messages software engineering computing education hci compiler error messages novice programmers compilers syntax errors anti-patterns cs1

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Towards Assessing the Readability of programming error messages 21

Towards Assessing the Readability of Programming Error Messa...

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23rd Australasian Computing Education Conference (ACE)

作者： Becker, Brett A. Denny, Paul Prather, James Pettit, Raymond Nix, Robert Mooney, Catherine Univ Coll Dublin Dublin Ireland Univ Auckland Auckland New Zealand Abilene Christian Univ Abilene TX 79699 USA Univ Virginia Charlottesville VA USA Oklahoma Christian Univ Edmond OK USA

ISBN: (纸本)9781450389761

programming error messages have proven to be notoriously problematic for novices who are learning to program. Although recent efforts have focused on improving message wording, these have been criticized for attempting to improve usability without first understanding and addressing readability. To date, there has been no research dedicated to the readability of programming error messages and how this could be assessed. In this paper we examine human-based assessments of programming error message readability and make two important contributions. First, we conduct an experiment using the top twenty most-frequent error messages in three popular programming languages (Python, Java, and C), revealing that human notions of readability are highly subjective and dependent on both programming experience and language familiarity. Both novices and experts agreed more about which messages are more readable, but disagreed more about which messages are not readable. Second, we leverage the data from this experiment to uncover several key factors that seem to affect message readability: message length, message tone, and use of jargon. We discuss how these factors can help guide future efforts to design a readability metric for programming error messages.

关键词： compiler error messages compiler design and implementation CS1 enhanced error messages HCI novice programmers programming programming error messages readability

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Using Large Language Models to Enhance programming error messages 2023

Using Large Language Models to Enhance Programming Error Mes...

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54th Annual ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE TS)

作者： Leinonen, Juho Hellas, Arto Sarsa, Sami Reeves, Brent Denny, Paul Prather, James Becker, Brett A. Aalto Univ Espoo Finland Abilene Christian Univ Abilene TX 79699 USA Univ Auckland Auckland New Zealand Univ Coll Dublin Dublin Ireland

ISBN: (纸本)9781450394314

A key part of learning to program is learning to understand programming error messages. They can be hard to interpret and identifying the cause of errors can be time-consuming. One factor in this challenge is that the messages are typically intended for an audience that already knows how to program, or even for programming environments that then use the information to highlight areas in code. Researchers have been working on making these errors more novice friendly since the 1960s, however progress has been slow. The present work contributes to this stream of research by using large language models to enhance programming error messages with explanations of the errors and suggestions on how to fix them. Large language models can be used to create useful and novice-friendly enhancements to programming error messages that sometimes surpass the original programming error messages in interpretability and actionability. These results provide further evidence of the benefits of large language models for computing educators, highlighting their use in areas known to be challenging for students. We further discuss the benefits and downsides of large language models and highlight future streams of research for enhancing programming error messages.

关键词： AI Codex compiler error messages large language models programming error messages syntax error messages

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First Steps Towards Predicting the Readability of programming error messages 2023

First Steps Towards Predicting the Readability of Programmin...

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54th Annual ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE TS)

作者： Prather, James Denny, Paul Becker, Brett A. Nix, Robert Reeves, Brent N. Randrianasolo, Arisoa S. Powell, Garrett Abilene Christian Univ Abilene TX 79699 USA Univ Auckland Auckland New Zealand Univ Coll Dublin Dublin Ireland Oklahoma Christian Univ Oklahoma City OK USA

ISBN: (纸本)9781450394314

Reading a programming error message is the first step in understanding what it is trying to tell the programmer about how to fix an error in their code. However, these are often difficult to read, especially for novices which is not surprising given that error messages in many of the most popular languages in which novices learn to code were not written with readability in mind. As a result, novices frequently struggle to understand them. This is a long-standing problem, with researchers highlighting concerns about programming error message readability over the last six decades. Very recent work has put forward evidence of the need for measuring readability in error messages and a framework for doing so. This framework consists of four factors of readability for programming error messages: message length, vocabulary, jargon, and sentence construction. We use this framework to implement an approach to automatically assess the readability of programming error messages. Using established readability factors as predictors in a machine learning model, we train several models using a dataset of C and Java error messages. We examine the performance of these models, and apply the best performing model to a previously published set of messages evaluated for readability by experts, non-experts and students. Our results validate the previously proposed readability factors, and our model classifies messages similarly to human raters. Finally, we discuss future work needed to improve the accuracy of the model.

关键词： compiler errors errors error messages machine learning PEMs programming error messages readability

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Not the Silver Bullet: LLM-enhanced programming error messages are Ineffective in Practice 24

Not the Silver Bullet: LLM-enhanced Programming Error Messag...

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6th Conference on United Kingdom and Ireland Computing Education Research

作者： Santos, Eddie Antonio Becker, Brett A. Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9798400711770

The sudden emergence of large language models (LLMs) such as ChatGPT has had a disruptive impact throughout the computing education community. LLMs have been shown to excel at producing correct code to CS1 and CS2 problems, and can even act as friendly assistants to students learning how to code. Recent work shows that LLMs demonstrate unequivocally superior results in being able to explain and resolve compiler error messages-for decades, one of the most frustrating parts of learning how to code. However, LLM-generated error message explanations have only been assessed by expert programmers in artificial conditions. This work sought to understand how novice programmers resolve programming error messages (PEMs) in a more realistic scenario. We ran a within-subjects study with n = 106 participants in which students were tasked to fix six buggy C programs. For each program, participants were randomly assigned to fix the problem using either a stock compiler error message, an expert-handwritten error message, or an error message explanation generated by GPT-4. Despite promising evidence on synthetic benchmarks, we found that GPT-4 generated error messages outperformed conventional compiler error messages in only 1 of the 6 tasks, measured by students' time-to-fix each problem. Handwritten explanations still outperform LLM and conventional error messages, both on objective and subjective measures.

关键词： AI compiler error messages computing education CS1 debugging feedback GenAI Generative AI GPT-4 C LLMs large language models novice programmers PEM programming error messages

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Compiler error messages Considered Unhelpful: The Landscape of Text-Based programming error Message Research 19

Compiler Error Messages Considered Unhelpful: The Landscape ...

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24th ACM Working Group Reports Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE-WGR)

作者： Becker, Brett A. Denny, Paul Pettit, Raymond Bouchard, Durell Bouvier, Dennis J. Harrington, Brian Kamil, Amir Karkare, Amey McDonald, Chris Osera, Peter-Michael Pearce, Janice L. Prather, James Univ Coll Dublin Dublin Ireland Univ Auckland Auckland New Zealand Univ Virginia Charlottesville VA USA Roanoke Coll Roanoke VA USA Southern Illinois Univ Edwardsville Edwardsville IL USA Univ Toronto Scarborough Scarborough ON Canada Univ Michigan Ann Arbor MI 48109 USA Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Univ Western Australia Perth WA Australia Grinnell Coll Grinnell IA 50112 USA Berea Coll Berea KY USA Abilene Christian Univ Abilene TX 79699 USA

ISBN: (纸本)9781450375672

Diagnostic messages generated by compilers and interpreters such as syntax error messages have been researched for over half of a century. Unfortunately, these messages which include error, warning, and run-time messages, present substantial difficulty and could be more effective, particularly for novices. Recent years have seen an increased number of papers in the area including studies on the effectiveness of these messages, improving or enhancing them, and their usefulness as a part of programming process data that can be used to predict student performance, track student progress, and tailor learning plans. Despite this increased interest, the long history of literature is quite scattered and has not been brought together in any digestible form. In order to help the computing education community (and related communities) to further advance work on programming error messages, we present a comprehensive, historical and state-of-the-art report on research in the area. In addition, we synthesise and present the existing evidence for these messages including the difficulties they present and their effectiveness. We finally present a set of guidelines, curated from the literature, classified on the type of evidence supporting each one (historical, anecdotal, and empirical). This work can serve as a starting point for those who wish to conduct research on compiler error messages, runtime errors, and warnings. We also make the bibtex file of our 300+ reference corpus publicly available. Collectively this report and the bibliography will be useful to those who wish to design better messages or those that aim to measure their effectiveness, more effectively.

关键词： compiler error messages considered harmful CS1 CS-1 design guidelines diagnostic error messages error messages human computer interaction HCI introduction to programming novice programmers programming errors programming error messages review run-time errors survey syntax errors warnings

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Compiler-Integrated, Conversational AI for Debugging CS1 Programs 2025

Compiler-Integrated, Conversational AI for Debugging CS1 Pro...

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56th Technical Symposium on Computer Science Education

作者： Renzella, Jake Vassar, Alexandra Solano, Lorenzo Lee Taylor, Andrew Univ New South Wales Sydney NSW Australia

ISBN: (纸本)9798400705311

Large Language Models (LLMs) present a transformative opportunity to address longstanding challenges in computing education. This paper presents a conversational AI extension to an LLM-enhanced C/C++ compiler which generates pedagogically sound programming error explanations. Our new tool, DCC Sidekick, retains compiler integration, allowing students to see their code, error messages, and stack frames alongside a conversational AI interface. Compiler context improves error explanations, and provides a seamless development experience. We present quantitative analyses of Sidekick's usage and engagement patterns in a large CS1 course. In the first seven weeks of use, 959 students initiated 11,222 DCC Sidekick sessions, generating 17,982 error explanations. Over half of all conversations occur outside of business hours, highlighting the value of these always-available tools. Early results indicate strong adoption of conversational AI debugging tools, demonstrating scalability in supporting large CS1 courses. We share implementation details and lessons learned, offering guidance to educators considering integrating AI tools with pedagogical guardrails.

关键词： programming error messages CS1 AI in Education Generative AI

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What makes a programming error message good? 22

What makes a programming error message good?

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4th Conference on United Kingdom and Ireland Computing Education Research (UKICER)

作者： Santos, Eddie Antonio Univ Coll Dublin Dublin Ireland

ISBN: (纸本)9781450397421

programming error messages (PEMs) often prove to be troublesome for novice programmers. Guidelines to improve PEMs often lack theoretical or empirical justification. This research will establish a theoretical foundation for what makes a "good" PEM, based on existing theories that have not yet been applied to PEMs. These findings will be applied to an existing programming environment and result in empirically-validated advice for language implementers.

关键词： Compilers Compiler error messages Computer Science Education Computing Education CS1 error messages HCI Human Factors Human Computer Interaction Novice Programmers programming errors programming error messages programming Languages Software Engineering Syntax errors

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Fine-Tuning Large Language Models for Better programming error Explanations 24

Fine-Tuning Large Language Models for Better Programming Err...

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24th Koli Calling

作者： Vassar, Alexandra Renzella, Jake Ross, Emily Taylor, Andrew Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9798400710384

This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: the suitability of university course forums in contributing to fine-tuning datasets, and how supervised fine-tuning can improve LLMs' alignment with educational principles such as constructivism. Initial findings suggest benefits in pedagogical alignment of LLMs, with deeper evaluations required.

关键词： programming error messages CS1 AI in CS1 AI in Education Generative AI LLM

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dcc --help: Transforming the Role of the Compiler by Generating Context-Aware error Explanations with Large Language Models 2024

dcc --help: Transforming the Role of the Compiler by Generat...

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55th ACM Technical Symposium on Computer Science Education (SIGCSE)

作者： Taylor, Andrew Vassar, Alexandra Renzella, Jake Pearce, Hammond Univ New South Wales Sydney NSW 2052 Australia

ISBN: (纸本)9798400704239

In the challenging field of introductory programming, high enrolments and failure rates drive us to explore tools and systems to enhance student outcomes, especially automated tools that scale to large cohorts. This paper presents and evaluates the dcc --help tool, an integration of a Large Language Model (LLM) into the Debugging C Compiler (DCC) to generate unique, novice-focused explanations tailored to each error. dcc --help prompts an LLM with contextual information of compile- and run-time error occurrences, including the source code, error location and standard compiler error message. The LLM is instructed to generate novice-focused, actionable error explanations and guidance, designed to help students understand and resolve problems without providing solutions. 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 analysed a subset of these error/explanation pairs to evaluate their properties, including conceptual correctness, relevancy, and overall quality. 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. Our findings, observations and reflections following deployment indicate that dcc --help provides novel opportunities for scaffolding students' introduction to programming.

关键词： CS1 AI in CS1 AI in Education Generative AI Large Language Models Compiler error messages Debugging error Message Enhancement programming error messages

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