Toward Semi-Automatic Misconception Discovery Using Code Embeddings

Shi, Yang; Shah, Krupal; Wang, Wengran; Marwan, Samiha; Penmetsa, Poorvaja; Price, Thomas W.

doi:10.1145/3448139.3448205

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…Many works have also built upon the code2vec model for various downstream tasks [26], [33], [34]. In their work, Shi et al [26] use the code2vec model on pairs of AST paths as input for the task of defect prediction.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

A Mocktail of Source Code Representations

Swarna¹,

Mathews²,

Vagavolu³

et al. 2021

Preprint

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Efficient representation of source code is essential for various software engineering tasks such as code search and code clone detection. One such technique for representing source code involves extracting paths from the AST and using a learning model to capture program properties. Code2vec is a commonly used path-based approach that uses an attention-based neural network to learn code embeddings which can then be used for various software engineering tasks. However, this approach uses only ASTs and does not leverage other graph structures such as Control Flow Graphs (CFG) and Program Dependency Graphs (PDG). Similarly, most recent approaches for representing source code still use AST and do not leverage semantic graph structures. Even though there exists an integrated graph approach (Code Property Graph) for representing source code, it has only been explored in the domain of software security. Moreover, it does not leverage the paths from the individual graphs. In our work, we extend the path-based approach code2vec to include semantic graphs, CFG, and PDG, along with AST, which is still largely unexplored in the domain of software engineering. We evaluate our approach on the task of METHODNAMING using a custom C dataset of 730K methods collected from 16 C projects from GitHub. In comparison to code2vec, our approach improves the F1 Score by 11% on the full dataset and up to 100% with individual projects. We show that semantic features from the CFG and PDG paths are indeed helpful. We envision that looking at a mocktail of source code representations for various software engineering tasks can lay the foundation for a new line of research and a re-haul of existing research.

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Section: Evaluation and Resultsmentioning

confidence: 99%

A Mocktail of Source Code Representations

Swarna¹,

Mathews²,

Vagavolu³

et al. 2021

Preprint

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“…Belief Miner investigates the concept of causal illusion, which is related to people's inherent bias to draw connections between coincidental events. A closely related concept is misconception, which is the inaccurate or wrong interpretation of concepts [69]. The terms misconception and misinformation are often used interchangeably.…”

Section: Reflecting On the User Studiesmentioning

confidence: 99%

Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations

Salim,

Hoque,

Mueller

2024

Proc. ACM Hum.-Comput. Interact.

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Causal belief is a cognitive practice that humans apply everyday to reason about cause and effect relations between factors, phenomena, or events. Like optical illusions, humans are prone to drawing causal relations between events that are only coincidental (i.e., causal illusions). Researchers in domains such as cognitive psychology and healthcare often use logistically expensive experiments to understand causal beliefs and illusions. In this paper, we propose Belief Miner, a crowdsourcing method for evaluating people's causal beliefs and illusions. Our method uses the (dis)similarities between the causal relations collected from the crowds and experts to surface the causal beliefs and illusions. Through an iterative design process, we developed a web-based interface for collecting causal relations from a target population. We then conducted a crowdsourced experiment with 101 workers on Amazon Mechanical Turk and Prolific using this interface and analyzed the collected data with Belief Miner. We discovered a variety of causal beliefs and potential illusions, and we report the design implications for future research.

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“…The work in [58] used least squares and ridge regression to automatically detect off-task behaviors in ITS. Other efforts have been made in discovering student misconceptions [59], [60], student behavior [61], learning styles [62], [63], and emotions [64].…”

Section: B Data-driven Approachesmentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

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There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-theart review of 11 years of research (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

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Toward Semi-Automatic Misconception Discovery Using Code Embeddings

Cited by 11 publications

References 23 publications

A Mocktail of Source Code Representations

A Mocktail of Source Code Representations

Belief Miner: A Methodology for Discovering Causal Beliefs and Causal Illusions from General Populations

Student Modeling and Analysis in Adaptive Instructional Systems

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