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Neural attribution for semantic bug-localization in student programs

Gupta, R and Kanade, A and Shevade, S (2019) Neural attribution for semantic bug-localization in student programs. In: 33rd Annual Conference on Neural Information Processing Systems, NeurIPS 2019, 8-14 December 2019, Vancouver; Canada.

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Providing feedback is an integral part of teaching. Most open online courses on programming make use of automated grading systems to support programming assignments and give real-time feedback. These systems usually rely on test results to quantify the programs' functional correctness. They return failing tests to the students as feedback. However, students may find it difficult to debug their programs if they receive no hints about where the bug is and how to fix it. In this work, we present NeuralBugLocator, a deep learning based technique, that can localize the bugs in a faulty program with respect to a failing test, without even running the program. At the heart of our technique is a novel tree convolutional neural network which is trained to predict whether a program passes or fails a given test. To localize the bugs, we analyze the trained network using a state-of-the-art neural prediction attribution technique and see which lines of the programs make it predict the test outcomes. Our experiments show that NeuralBugLocator is generally more accurate than two state-of-the-art program-spectrum based and one syntactic difference based bug-localization baselines. © 2019 Neural information processing systems foundation. All rights reserved.

Item Type: Conference Paper
Publication: Advances in Neural Information Processing Systems
Publisher: Neural information processing systems foundation
Additional Information: The copyright of this article belongs to Neural information processing systems foundation
Keywords: Convolutional neural networks; Deep learning; Forecasting; Grading; Online systems; Real time systems; Semantics; Software testing; Students; Testing, Automated grading systems; Bug localizations; Functional correctness; Neural prediction; Open online course; Programming assignments; Real-time feedback; State of the art, Program debugging
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 22 Sep 2020 06:10
Last Modified: 28 Aug 2022 10:22
URI: https://eprints.iisc.ac.in/id/eprint/66565

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