SeSaMe: A Data Set of Semantically Similar Java Methods

Kamp, Marius; Kreutzer, Patrick; Philippsen, Michæl

doi:10.1109/msr.2019.00079

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…There are several popular clone related datasets, which can be used for clone search. These datasets include BigCloneBench [36], [37], Project CodeNet (PCN) [38], SeSaMe [50], Pedagogical programming Open Judge (POJ-104) [51] and Google Code Jam(GCJ) [52]. BigCloneBench dataset contains references of clone methods belonging to different functionality types that exist in IJaDataset.…”

Section: B Clone Datasetsmentioning

confidence: 99%

Clone-Seeker: Effective Code Clone Search Using Annotations

et al. 2022

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Source code search plays an important role in software development, e.g. for exploratory development or opportunistic reuse of existing code from a code base. Often, exploration of different implementations with the same functionality is needed for tasks like automated software transplantation, software diversification, and software repair. Code clones, which are syntactically or semantically similar code fragments, are perfect candidates for such tasks. Searching for code clones involves a given search query to retrieve the relevant code fragments. We propose a novel approach called Clone-Seeker that focuses on utilizing clone class features in retrieving code clones. For this purpose, we generate metadata for each code clone in the form of a natural language document. The metadata includes a pre-processed list of identifiers from the code clones augmented with a list of keywords indicating the semantics of the code clone. This keyword list can be extracted from a manually annotated general description of the clone class, or automatically generated from the source code of the entire clone class. This approach helps developers to perform code clone search based on a search query written either as source code terms, or as natural language. With various experiments, we show that (1) Clone-Seeker is effective in finding clones from BigCloneBench dataset by applying code queries and natural language queries; 2) Clone-Seeker has a higher recall when searching for semantic code clones (i.e., Type-4) in BigCloneBench than the state-of-the-art; 3) Clone-Seeker is a generalized technique as it is effective in finding clones in Project CodeNet dataset by applying code queries and natural language queries. 4) Clone-Seeker with manual annotation outperforms other variants in finding clones on the basis of natural language queries.

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Section: B Clone Datasetsmentioning

confidence: 99%

Clone-Seeker: Effective Code Clone Search Using Annotations

et al. 2022

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“…≤ 25 lines), have nontrivial or branching logic with no syntactic errors, and rely on built-in features or standard libraries. To find candidates, we explored code clone benchmarks [18], [19], [20], [21], [22], code search engines, open-source platforms like GitHub, and practice websites like Codewars 1 and LeetCode. 2 We manually modified some snippets to control our variables but wrote none from scratch.…”

Section: B Artifact Collectionmentioning

confidence: 99%

Understanding Similar Code through Comparative Comprehension

Middleton

Stolee

2022

2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

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Any problem in code may have multiple solutions that differ in details large and small. Because modern software development is characterized by an abundance of searchable and reusable code, effective developers must be able to judge not only the meaning of new algorithms but also the differences between alternatives. Therefore, we use a multi-method study to explore how developers perform comparative comprehensionthe cognitive activity of understanding how algorithms behave relative to each other.To explore how developers compare code, we performed a controlled experiment with 16 developers in a mixed think-aloud and interview format and another 95 developers in a survey format. In this experiment, participants investigated whether a pair of code snippets would demonstrate equivalent behavior when run, controlling for differences in behavior, programming languages, algorithmic structures, and meaningful names. Overall, our results describe how comparison fits into learning, reviewing, and reusing code. Our task observations shed light on how developers move between code similarities at different levels-textual, structural, and schematic-when simultaneously inspecting multiple snippets. In our experiment, developers made more accurate conclusions about behavior given similar languages and structures, with names acting as additional evidence in interaction with other cues, but they also overestimated whether behavior is equivalent in many cases. From this, we identify challenges developers face in comprehending alternatives and we highlight opportunities to better support developers in comparison activities.

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“…Our study is carried out on two open-source datasets, SeSaMe [16] and GCJ [47]. SeSaMe is collected from 11 real-world software repositories, including JDK11 and eclipse.…”

Section: Data Collectionmentioning

confidence: 99%

SEED: Semantic Graph based Deep detection for type-4 clone

Xue,

Liu,

Zeng

et al. 2021

Preprint

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Background: Type-4 clones refer to a pair of code snippets with similar functionality but written in different syntax, which challenges the existing code clone detection techniques. Previous studies, however, highly rely on syntactic structures and textual tokens, which cannot precisely represent the semantic information of code and might introduce nonnegligible noise into the detection models. Aims: To overcome these limitations, we explore an effective semantic-based solution for Type-4 clone detection. Additionally, we conducted an empirical study on the characteristics of Type-4 clone pairs. We found that NOT all tokens contain semantics that the Type-4 clone detection required. Operators and API calls emerge as distinct candidates for Type-4 code semantic representation. Method: To bridge this gap, we design a novel semantic graph based deep detection approach, called SEED. For a pair of code snippets, SEED constructs a semantic graph of each code snippet based on intermediate representation to represent the code functionality more precisely compared to the representations based on lexical and syntactic analysis. To accommodate the characteristics of Type-4 clones, a semantic graph is constructed focusing on the operators and API calls instead of all tokens. Then, SEED generates the feature vectors by using the graph deep neural network and performs code clone detection based on the similarity among the vectors. Results: Extensive experiments show that our approach significantly outperforms two baseline approaches over

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SeSaMe: A Data Set of Semantically Similar Java Methods

Cited by 8 publications

References 28 publications

Clone-Seeker: Effective Code Clone Search Using Annotations

Clone-Seeker: Effective Code Clone Search Using Annotations

Understanding Similar Code through Comparative Comprehension

SEED: Semantic Graph based Deep detection for type-4 clone

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