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GraphQL response data volume prediction based on code2vec and AutoML

INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2024年第3期20卷 268-288页

作者： Zhang, Feng Wei, Youliang Feng, Tao Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China

PurposeGraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query ***/methodology/approachThis paper proposes a GraphQL response data volume prediction approach based on code2vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of code2vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML *** on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, ***/valueThis paper proposes an approach that combines code2vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

关键词： GraphQL Data volume prediction code2vec AutoML

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Grammatical Evolution with code2vec 6th

Grammatical Evolution with Code2vec

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26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar

作者： Kowalczykiewicz, Michal Lipinski, Piotr Univ Wroclaw Inst Comp Sci Computat Intelligence Res Grp Wroclaw Poland

ISBN: (纸本)9783031295720;9783031295737

This paper concerns using the code2vec vector embeddings of the source code to improve automatic source code generation in Grammatical Evolution. Focusing on a particular programming language, such as Java in the research presented, and being able to represent each Java function in the form of a continuous vector in a linear space by the code2vec model, GE gains some additional knowledge on similarities between constructed functions in the linear space instead of semantic similarities, which are harder to process. We propose a few improvements to the regular GE algorithm, including a code2vec-based initialization of the evolutionary algorithm and a code2vec-based crossover operator. Computational experiments confirm the efficiency of the approach proposed on a few typical benchmarks.

关键词： Grammatical Evolution Source code vector Embeddings code2vec

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code Component Retrieval Using code2vec 5

Code Component Retrieval Using Code2Vec

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5th International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC)

作者： RamyaSree, B. Ramakrishna, Bajjuri Harshitha, M., I Kavya, Amma Reshvanth, Paladugu Rao, N. V. Krishna Inst Aeronaut Engn Dundigal Dept Comp Sci & Engn Hyderabad India SRM Inst Sci & Technol Dept Comp Sci & Engn Kattankulathur 603203 Tamil Nadu India

ISBN: (纸本)9781665426428

The system helps to gather the code relevant compound by using code2vec tokenization concept. This research work has used a embedding code, which go through code2vec model. In this model code components are converted to tokens and give a numeric representation using code embedding technique. It retrieves the most accurate code and obtains the code component by their prediction score. Here, the score is calculated by using cosine similarity for obtaining the best results from the dataset.

关键词： code2vec Embedding techniques Cosine similarity Prediction score

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Embedding Java Classes with code2vec: Improvements from Variable Obfuscation 17

Embedding Java Classes with code2vec: Improvements from Vari...

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IEEE/ACM 17th International Conference on Mining Software Repositories (MSR)

作者： Compton, Rhys Frank, Eibe Patros, Panos Koay, Abigail Univ Waikato Hamilton New Zealand

ISBN: (纸本)9781450375177

Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared(1) for further ML research on source code.

关键词： machine learning neural networks code2vec source code code obfuscation

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Evaluating the Effectiveness of code2vec for Bug Prediction When Considering That Not All Bugs Are the Same

Evaluating the Effectiveness of Code2Vec for Bug Prediction ...

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作者： Baron, Kilby University of Waterloo

学位级别：硕士

Bug prediction is an area of research focused on predicting where in a software project future bugs will occur. The purpose of bug prediction models is to help companies spend their quality assurance resources more efficiently by prioritizing the testing of the most defect prone entities. Most bug prediction models are only concerned with predicting whether an entity has a bug, or how many bugs an entity will have, which implies that all bugs have the same importance. In reality, bugs can have vastly different origins, impacts, priorities, and costs; therefore, bug prediction models could potentially be improved if they were able to give an indication of which bugs to prioritize based on an organization's needs. This paper evaluates a possible method for predicting bug attributes related to cost by analyzing over 33,000 bugs from 11 different projects. If bug attributes related to cost can be predicted, then bug prediction models can use the approach to improve the granularity of their results. The cost metrics in this study are bug priority, the experience of the developer who fixed the bug, and the size of the bug fix. First, it is shown that bugs differ along each cost metric, and prioritizing buggy entities along each of these metrics will produce very different results. We then evaluate two methods of predicting cost metrics: traditional deep learning models, and semantic learning models. The results of the analysis found evidence that traditional independent variables show potential as predictors of cost metrics. The semantic learning model was not as successful, but may show more effectiveness in future iterations.

关键词： bug prediction code2vec

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Semantic code search using code2vec: A bag-of-paths model

Semantic code search using Code2Vec: A bag-of-paths model

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作者： Arumugam, Lakshmanan University of Waterloo

学位级别：硕士

The world is moving towards an age centered around digital artifacts created by individuals, not only are the digital artifacts being created at an alarming rate, also the software to manage such artifacts is increasing than ever. Majority of any software is infused with large number of source code files. Therefore, code search has become an intrinsic part of software development process today and the universe of source code is only growing. Although, there are many general purpose search engines such as Google, Bing and other web search engines that are used for code search, such search engines are not dedicated only for software code search. Moreover, keyword based search may not return relevant documents when the search keyword is not present in the candidate documents. And, it does not take into account the semantic and syntactic properties of software artifacts such as source code. Semantic search (in the context of software engineering) is an emerging area of research that explores the efficiency of searching a code base using natural language queries. In this thesis, we aim to provide developers with the ability to locate source code blocks/snippets through semantic search that is built using neural models. Neural models are capable of representing natural language using vectors that have been shown to carry semantic meanings and are being used in various NLP tasks. Specifically, we want to use code2vec, a model that learns distributed representations of source code called code embeddings, to evaluate its performance against the task of semantically searching code snippets. The main idea behind using code2vec is that source code is structurally different from natural language and a model that uses the syntactic nature of source code can be helpful in learning the semantic properties. We pair code2vec with other neural models that represents natural language through vectors to create a hybrid model that outperforms previous benchmark baseline models developed

关键词： semantic search search code2vec codesearchnet reranking source code retrieval snippets developers

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user2code2vec: Embeddings for Profiling Students Based on Distributional Representations of Source code 19

user2code2vec: Embeddings for Profiling Students Based on Di...

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9th International Conference on Learning Analytics and Knowledge (LAK)

作者： Azcona, David Arora, Piyush Hsiao, I-Han Smeaton, Alan Dublin City Univ Insight Ctr Data Analyt Dublin Ireland Dublin City Univ ADAPT Ctr Dublin Ireland Arizona State Univ Comp Syst & Informat Tempe AZ USA

ISBN: (纸本)9781450362566

In this work, we propose a new methodology to profile individual students of computer science based on their programming design using a technique called embeddings. We investigate different approaches to analyze user source code submissions in the Python language. We compare the performances of different source code vectorization techniques to predict the correctness of a code submission. In addition, we propose a new mechanism to represent students based on their code submissions for a given set of laboratory tasks on a particular course. This way, we can make deeper recommendations for programming solutions and pathways to support student learning and progression in computer programming modules effectively at a Higher Education Institution. Recent work using Deep Learning tends to work better when more and more data is provided. However, in Learning Analytics, the number of students in a course is an unavoidable limit. Thus we cannot simply generate more data as is done in other domains such as FinTech or Social Network Analysis. Our findings indicate there is a need to learn and develop better mechanisms to extract and learn effective data features from students so as to analyze the students' progression and performance effectively.

关键词： user2code2vec code2vec code Embeddings Distributed Representations Representation Learning for Source code Machine Learning Computer Science Education

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基于代码和描述文本相融合的软件分类研究

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华东师范大学学报(自然科学版) 2025年第1期 46-58页

作者：陈宇航王世宙汤正婷陈良育姜宁康华东师范大学软件工程学院上海200062

第三方软件系统在现代软件开发过程中有着重要的作用.软件开发人员根据需求,在第三方软件库中检索合适的依赖库来构建软件,可避免许多重复工作,加快开发过程.然而,检索第三方依赖库的过程可能会很困难.通常第三方软件库提供预设的标签(... 详细信息

第三方软件系统在现代软件开发过程中有着重要的作用.软件开发人员根据需求,在第三方软件库中检索合适的依赖库来构建软件,可避免许多重复工作,加快开发过程.然而,检索第三方依赖库的过程可能会很困难.通常第三方软件库提供预设的标签(类别)给软件开发人员进行查找,但是如果一个软件的预设标签被错误地标注,软件开发人员就无法查找到其需要的库,这势必会影响开发过程.提出了一种软件分类模型来解决上述挑战,模型结合方法向量、方法重要性和文本向量,将未知类别的软件分类到已知类别.鉴于此问题尚未有公开的数据集,为此建立了一个数据集并公开,此数据集包含来自Maven存储库的30种类别的120个软件系统.在此自建数据集上对提出的分类模型进行了测试,预测类别的准确度对于1个候选者的情况(top-1)为70%,对于3个候选者的情况(top-3)则达到了90%.实验结果表明,所提模型可以有效用于对开源存储库中的软件系统分类,辅助软件开发人员快速查找第三方库.

关键词：软件分类第三方软件系统方法重要性分数 code2vec

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Identifying Incorrect Patches in Program Repair Based on Meaning of Source code

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IEEE ACCESS 2022年 10卷 12012-12030页

作者： Quang-Ngoc Phung Kim, Misoo Lee, Eunseok Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea Sungkyunkwan Univ Inst Software Convergence Suwon 16419 South Korea Sungkyunkwan Univ Coll Comp Suwon 16419 South Korea

Automatic Program Repair (APR) techniques have shown the potential of reducing debugging costs while improving software quality by generating patches for fixing bugs automatically. However, they often generate many overfitting patches which pass only a specific test-suite but do not fix the bugs correctly. This paper proposes MIPI, a novel approach to reducing the number of overfitting patches generated in the APR. We leverage recent advances in deep learning to exploit the similarity between the patched method's name (which often encloses the developer's intention about the code) and the semantic meaning of the method's body (which represents the actual implemented behavior) for identifying and removing overfitting patches generated by APR tools. Experiments with a large dataset of patches for QuixBugs and Defects4J programs show the promise of our approach. Specifically, in a total of 1,191 patches generated by 23 existing APR tools, MIPI successfully filters out 254 (32%) of the total 797 overfitting patches with a precision of 90% while preserving 93% of the correct patches. MIPI is more precise and less damaging to the APR than existing heuristic patch assessment techniques, achieving a higher recall than automated testing-based techniques that do not have access to the test oracle. In addition, MIPI is highly complementary to existing automated patch assessment techniques.

关键词： codes Software Computer bugs Test pattern generators Semantics Maintenance engineering Task analysis Automated program repair APR patch correctness developer intentions code2vec bert

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Software system comparison with semantic source code embeddings

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EMPIRICAL SOFTWARE ENGINEERING 2022年第3期27卷 1-33页

作者： Karakatic, Saso Milosevic, Aleksej Hericko, Tjasa Univ Maribor Fac Elect Engn & Comp Sci Koroska Cesta 46 Maribor 2000 Slovenia

This paper presents a novel approach for comparing software systems by calculating the robust Hausdorff distance between semantic source code embeddings of individual software components, i.e., methods. The proposed approach represents each software as a set of vectors, where every vector is a semantic source code embedding of a particular method. The code embeddings are constructed from abstract syntax trees of the methods with the help of attention-based neural network models that capture the semantics of the methods. Previous research has shown that comparing semantic source code embeddings can reveal semantic relationships between the two methods. We utilize this characteristic to estimate the semantic similarity between the two software systems by computing the robust Hausdorff distance. In the experiment, a pre-trained code2vec neural network model is used to create the source code vector representations of several open-source Java-based libraries. Several variations of the robust Hausdorff distance are evaluated. The results show that the proposed approach can effectively estimate the semantic similarity, reflecting the software library's scopes, software evolution, and individual parts (e.g., packages) of those libraries.

关键词： Source code embeddings Software comparison code similarity code2vec Machine learning

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