Natural Software Revisited

Rahman, Musfiqur; Palani, Dharani; Rigby, Peter C.

doi:10.1109/icse.2019.00022

Cited by 42 publications

(33 citation statements)

References 46 publications

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“…Given a source code file, PMD checks if source code violates the rules and reports warnings which indicate the violated rules, priority, and the corresponding lines in that file. Similar to prior work [66,67], we identify the lines reported in the warnings as defect-prone lines. We rank the defect-prone lines based on the priority of the warnings where a priority of 1 indicates the highest priority and 4 indicates the lowest priority.…”

Section: Baseline Approachesmentioning

confidence: 72%

“…To do so, for each source code file in defect datasets, we first apply a set of regular expressions to remove non-alphanumeric characters such as semi-colon (;), equal sign (=). As suggested by Rahman and Rigby [66], removing these non-alphanumeric characters will ensure that the analyzed code tokens will not be artificially repetitive. Then, we extract code tokens in the files using the Countvectorize function of the Scikit-Learn library.…”

Section: (Step 1) Extracting Featuresmentioning

confidence: 99%

“…Static Analysis. Prior work shows that a static analysis tool can be used to identify defect-prone lines [25,42,47,64,66,67]. Habib and Pradel [25] found that static bug detectors are certainly worthwhile to detect real-world bugs.…”

Section: Baseline Approachesmentioning

confidence: 99%

“…Hence, we use two static analysis tools, i.e., PMD [12] and ErrorProne [2] as our baseline approaches. PMD: We use PMD [12] which is often used in previous research [42,47,64,66,67]. We did not use FINDBUGS since prior studies [66,67] show that the performance of PMD and FINDBUGS are comparable.…”

Section: Baseline Approachesmentioning

confidence: 99%

“…PMD: We use PMD [12] which is often used in previous research [42,47,64,66,67]. We did not use FINDBUGS since prior studies [66,67] show that the performance of PMD and FINDBUGS are comparable. PMD is a static analysis tool that identifies common errors based on a set of predefined rules with proven properties.…”

Section: Baseline Approachesmentioning

confidence: 99%

See 4 more Smart Citations

Predicting Defective Lines Using a Model-Agnostic Technique

Wattanakriengkrai

Thongtanunam

Tantithamthavorn

et al. 2022

IIEEE Trans. Software Eng.

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Defect prediction models are proposed to help a team prioritize source code areas files that need Software Quality Assurance (SQA) based on the likelihood of having defects. However, developers may waste their unnecessary effort on the whole file while only a small fraction of its source code lines are defective. Indeed, we find that as little as 1%-3% of lines of a file are defective. Hence, in this work, we propose a novel framework (called LINE-DP) to identify defective lines using a model-agnostic technique, i.e., an Explainable AI technique that provides information why the model makes such a prediction. Broadly speaking, our LINE-DP first builds a file-level defect model using code token features. Then, our LINE-DP uses a state-of-the-art model-agnostic technique (i.e., LIME) to identify risky tokens, i.e., code tokens that lead the file-level defect model to predict that the file will be defective. Then, the lines that contain risky tokens are predicted as defective lines. Through a case study of 32 releases of nine Java open source systems, our evaluation results show that our LINE-DP achieves an average recall of 0.61, a false alarm rate of 0.47, a top 20%LOC recall of 0.27, and an initial false alarm of 16, which are statistically better than six baseline approaches. Our evaluation shows that our LINE-DP requires an average computation time of 10 seconds including model construction and defective identification time. In addition, we find that 63% of defective lines that can be identified by our LINE-DP are related to common defects (e.g., argument change, condition change). These results suggest that our LINE-DP can effectively identify defective lines that contain common defects while requiring a smaller amount of inspection effort and a manageable computation cost. The contribution of this paper builds an important step towards line-level defect prediction by leveraging a model-agnostic technique.

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Section: Baseline Approachesmentioning

confidence: 72%

Section: (Step 1) Extracting Featuresmentioning

confidence: 99%

Section: Baseline Approachesmentioning

confidence: 99%

Section: Baseline Approachesmentioning

confidence: 99%

Section: Baseline Approachesmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Defective Lines Using a Model-Agnostic Technique

Wattanakriengkrai

Thongtanunam

Tantithamthavorn

et al. 2022

IIEEE Trans. Software Eng.

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A graph sequence neural architecture for code completion with semantic structure features

Yang

Fan

et al. 2021

J Software Evolu Process

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Code completion plays an important role in intelligent software development for accelerating coding efficiency. Recently, the prediction models based on deep learning have achieved good performance in code completion task. However, the existing models cannot avoid three drawbacks: (i) In the existing models, the code representation loses the information (parent-child information between nodes) and lacks many effective features (orientation between nodes). (ii) The known code structure information is not fully utilized, which will cause the model to generate completely irrelevant results. (iii) Simple sequence modeling ignores repeated patterns and structural information. Besides, previous works cannot capture the characteristics of correlation and directionality between nodes. In this paper, we propose a Code Completion approach named CC-GGNN, which is graph model based on Gated Graph Neural Networks (GGNNs) to address the problems. We introduce a new architecture to obtain the effective code features from code representation. In order to utilize the known information, we propose Classification Mechanism, which classifies the representation of the node using the known parent node and constructs training graph in the model. The experimental results show that our model outperforms the state-ofthe-art methods MRR@5 at most 9.2% and ACC at most 11.4% in datasets.

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Deep Generation of Coq Lemma Names Using Elaborated Terms

Nie

Palmskog

et al. 2020

Automated Reasoning

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Coding conventions for naming, spacing, and other essentially stylistic properties are necessary for developers to effectively understand, review, and modify source code in large software projects. Consistent conventions in verification projects based on proof assistants, such as Coq, increase in importance as projects grow in size and scope. While conventions can be documented and enforced manually at high cost, emerging approaches automatically learn and suggest idiomatic names in Java-like languages by applying statistical language models on large code corpora. However, due to its powerful language extension facilities and fusion of type checking and computation, Coq is a challenging target for automated learning techniques. We present novel generation models for learning and suggesting lemma names for Coq projects. Our models, based on multi-input neural networks, are the first to leverage syntactic and semantic information from Coq's lexer (tokens in lemma statements), parser (syntax trees), and kernel (elaborated terms) for naming; the key insight is that learning from elaborated terms can substantially boost model performance. We implemented our models in a toolchain, dubbed Roosterize, and applied it on a large corpus of code derived from the Mathematical Components family of projects, known for its stringent coding conventions. Our results show that Roosterize substantially outperforms baselines for suggesting lemma names, highlighting the importance of using multi-input models and elaborated terms.

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Natural Software Revisited

Cited by 42 publications

References 46 publications

Predicting Defective Lines Using a Model-Agnostic Technique

Predicting Defective Lines Using a Model-Agnostic Technique

A graph sequence neural architecture for code completion with semantic structure features

Deep Generation of Coq Lemma Names Using Elaborated Terms

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