Automated program repair has been used to provide feedback for incorrect student programming assignments, since program repair captures the code modification needed to make a given buggy program pass a given test-suite. Existing student feedback generation techniques are limited because they either require manual effort in the form of providing an error model, or require a large number of correct student submissions to learn from, or suffer from lack of scalability and accuracy. In this work, we propose a fully automated approach for generating student program repairs in real-time. This is achieved by first re-factoring all available correct solutions to semantically equivalent solutions. Given an incorrect program, we match the program with the closest matching refactored program based on its control flow structure. Subsequently, we infer the input-output specifications of the incorrect program's basic blocks from the executions of the correct program's aligned basic blocks. Finally, these specifications are used to modify the blocks of the incorrect program via search-based synthesis. Our dataset consists of almost 1,800 real-life incorrect Python program submissions from 361 students for an introductory programming course at a large public university. Our experimental results suggest that our method is more effective and efficient than recently proposed feedback generation approaches. About 30% of the patches produced by our tool Refactory are smaller than those produced by the state-of-art tool Clara, and can be produced given fewer correct solutions (often a single correct solution) and in a shorter time. We opine that our method is applicable not only to programming assignments, and could be seen as a general-purpose program repair method that can achieve good results with just a single correct reference solution.

/pdf/re-factoring-based-program-repair-applied-to-programming-1saywe5jkp.pdf

Re-factoring based program repair applied to programming assignments

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, grading approach, language paradigm, degree of automation and evaluation techniques. Most papers grade the correctness of object-oriented assignments. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students. However, compared to correctness grading, few tools assess readability, maintainability, or documentation, focusing solely on the presence of documentation, not documentation quality. 

/pdf/automated-grading-and-feedback-tools-for-programming-3dxbmkc0.pdf

Automated Grading and Feedback Tools for Programming Education: A
  Systematic Review

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, grading approach, language paradigm, degree of automation and evaluation techniques. Most papers grade the correctness of object-oriented assignments. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students. However, compared to correctness grading, few tools assess readability, maintainability, or documentation, focusing solely on the presence of documentation, not documentation quality.

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

We present a new technique for automatically detecting logical errors in functional programming assignments. Compared to syntax or type errors, detecting logical errors remains largely a manual process that requires hand-made test cases. However, designing proper test cases is nontrivial and involves a lot of human effort. Furthermore, manual test cases are unlikely to catch diverse errors because instructors cannot predict all corner cases of diverse student submissions. We aim to reduce this burden by automatically generating test cases for functional programs. Given a reference program and a student's submission, our technique generates a counter-example that captures the semantic difference of the two programs without any manual effort. The key novelty behind our approach is the counter-example generation algorithm that combines enumerative search and symbolic verification techniques in a synergistic way. The experimental results show that our technique is able to detect 88 more errors not found by mature test cases that have been improved over the past few years, and performs better than the existing property-based testing techniques. We also demonstrate the usefulness of our technique in the context of automated program repair, where it effectively helps to eliminate test-suite-overfitted patches.

https://dl.acm.org/doi/pdf/10.1145/3360614

Automatic and scalable detection of logical errors in functional programming assignments

Automatic program repair (APR) is a rapidly advancing field of software engineering that aims to supplement or replace manual bug fixing with an automated tool. For APR to be successfully adopted in industry, it is vital that APR tools respond to developer needs and preferences. However, very little research has considered developers&#x0027; general attitudes to APR or developers&#x0027; current bug fixing practices (the activity APR aims to replace). This paper responds to this gap by reporting on a survey of 386 software developers about their bug finding and fixing practices and experiences, and their instinctive attitudes towards APR. We find that bug finding and fixing is not necessarily as onerous for developers as has often been suggested, being rated as more satisfying than developers&#x0027; general work. The fact that developers derive satisfaction and benefit from bug fixing indicates that APR adoption is not as simple as APR replacing an unwanted activity. When it comes to potential APR approaches, we find a strong preference for developers being kept in the loop (for example, choosing between different fixes or validating fixes) as opposed to a fully automated process. This suggests that advances in APR should be careful to consider the agency of the developer, as well as what information is presented to developers alongside fixes. It also indicates that there are key barriers related to trust that would need to be overcome for full scale APR adoption, supported by the fact that even those developers who stated that they were positive about APR listed several caveats and concerns. We find very few statistically significant relationships between particular demographic variables (for example, developer experience, age, education) and key attitudinal variables, suggesting that developers&#x0027; instinctive attitudes towards APR are little influenced by experience level but are held widely across the developer community. 

/pdf/how-do-developers-i-really-i-feel-about-bug-fixing-2b75dekt.pdf

How do Developers Really Feel About Bug Fixing? Directions for Automatic Program Repair

We present FixML, a system for automatically generating feedback on logical errors in functional programming assignments. As functional languages have been gaining popularity, the number of students enrolling functional programming courses has increased significantly. However, the quality of feedback, in particular for logical errors, is hardly satisfying. To provide personalized feedback on logical errors, we present a new error-correction algorithm for functional languages, which combines statistical error-localization and type-directed program synthesis enhanced with components reduction and search space pruning using symbolic execution. We implemented our algorithm in a tool, called FixML, and evaluated it with 497 students’ submissions from 13 exercises, including not only introductory but also more advanced problems. Our experimental results show that our tool effectively corrects various and complex errors: it fixed 43% of the 497 submissions in 5.4 seconds on average and managed to fix a hard-to-find error in a large submission, consisting of 154 lines. We also performed user study with 18 undergraduate students and confirmed that our system actually helps students to better understand their programming errors.

https://dl.acm.org/doi/pdf/10.1145/3276528

Automatic diagnosis and correction of logical errors for functional programming assignments

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of {limit} columns in the table. Remove at least 1 column to add or create another one.

Junho Lee

Author Tools

Chat about Author

Papers

Automatic diagnosis and correction of logical errors for functional programming assignments