Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering最新文献_第6页

reformulator: Automated Refactoring of the N+1 Problem in Database-Backed Applications reformulator:数据库支持应用程序中N+1问题的自动重构

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3556911

Alexi Turcotte, Mark W. Aldrich, F. Tip

{"title":"reformulator: Automated Refactoring of the N+1 Problem in Database-Backed Applications","authors":"Alexi Turcotte, Mark W. Aldrich, F. Tip","doi":"10.1145/3551349.3556911","DOIUrl":"https://doi.org/10.1145/3551349.3556911","url":null,"abstract":"An Object-Relational Mapping (ORM) provides an object-oriented interface to a database and facilitates the development of database-backed applications. In an ORM, programmers do not need to write queries in a separate query language such as SQL, they instead write ordinary method calls that are mapped by the ORM to database queries. This added layer of abstraction hides the significant performance cost of database operations, and misuse of ORMs can lead to far more queries being generated than necessary. Of particular concern is the infamous “N+1 problem”, where an initial query yields N results that are used to issue N subsequent queries. This anti-pattern is prevalent in applications that use ORMs, as it is natural to iterate over collections in object-oriented languages. However, iterating over data that originates from a database and calling an ORM method in each iteration may result in suboptimal performance. In such cases, it is often possible to reduce the number of round-trips to the database by issuing a single, larger query that fetches all desired results at once. We propose an approach for automatically refactoring applications that use ORMs to eliminate instances of the “N+1 problem”, which relies on static analysis to detect data flow between ORM API calls. We implement this approach in a tool called reformulator, targeting the Sequelize ORM in JavaScript, and evaluate it on 8 JavaScript projects. We found 44 N+1 query pairs in these projects, and reformulator refactored all of them successfully, resulting in improved performance (up to 7.67x) while preserving program behavior. Further experiments demonstrate that the relative performance improvements grew as the database size was increased (up to 38.58x), and show that front-end page load times were also improved.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116479929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

DyTRec: A Dynamic Testing Recommendation tool for Unity-based Virtual Reality Software DyTRec:基于unity的虚拟现实软件动态测试推荐工具

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3560510

Xue Qin, Foyzul Hassan

引用次数: 0

Detecting Build Conflicts in Software Merge for Java Programs via Static Analysis 通过静态分析检测Java程序软件合并中的构建冲突

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3556950

Sheikh Shadab Towqir, Bowen Shen, Muhammad Ali Gulzar, Na Meng

{"title":"Detecting Build Conflicts in Software Merge for Java Programs via Static Analysis","authors":"Sheikh Shadab Towqir, Bowen Shen, Muhammad Ali Gulzar, Na Meng","doi":"10.1145/3551349.3556950","DOIUrl":"https://doi.org/10.1145/3551349.3556950","url":null,"abstract":"In software merge, the edits from different branches can textually overlap (i.e., textual conflicts) or cause build and test errors (i.e., build and test conflicts), jeopardizing programmer productivity and software quality. Existing tools primarily focus on textual conflicts; few tools detect higher-order conflicts (i.e., build and test conflicts). However, existing detectors of build conflicts are limited. Due to their heavy usage of automatic build, current detectors (e.g., Crystal) only report build errors instead of identifying the root causes; developers have to manually locate conflicting edits. These detectors only help when the branches-to-merge have no textual conflict. We present a new static analysis-based approach Bucond (“build conflict detector”). Given three code versions in a merging scenario: base b, left l, and right r, Bucond models each version as a graph, and compares graphs to extract entity-related edits (e.g., class renaming) in l and r. We believe that build conflicts occur when certain edits are co-applied to related entities between branches. Bucond realizes this insight via pattern matching to identify any cross-branch edit combination that can trigger build conflicts (e.g., one branch adds a reference to field F while the other branch removes F). We systematically explored and devised 57 patterns, covering 97% of the build conflicts in our experiments. Our evaluation shows Bucond to complement build-based detectors, as it (1) detects conflicts with 100% precision and 88%–100% recall, (2) locates conflicting edits, and (3) works well when those detectors do not.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127952962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation 基于混合关注网络的大规模内容推荐多视图学习模型

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559496

Ge Fan, Chaoyun Zhang, Kai Wang, Junyang Chen

{"title":"MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation","authors":"Ge Fan, Chaoyun Zhang, Kai Wang, Junyang Chen","doi":"10.1145/3551349.3559496","DOIUrl":"https://doi.org/10.1145/3551349.3559496","url":null,"abstract":"Industrial recommender systems usually employ multi-source data to improve the recommendation quality, while effectively sharing information between different data sources remain a challenge. In this paper, we introduce a novel Multi-View Approach with Hybrid Attentive Networks (MV-HAN) for contents retrieval at the matching stage of recommender systems. The proposed model enables high-order feature interaction from various input features while effectively transferring knowledge between different types. By employing a well-placed parameters sharing strategy, the MV-HAN substantially improves the retrieval performance in sparse types. The designed MV-HAN inherits the efficiency advantages in the online service from the two-tower model, by mapping users and contents of different types into the same features space. This enables fast retrieval of similar contents with an approximate nearest neighbor algorithm. We conduct offline experiments on several industrial datasets, demonstrating that the proposed MV-HAN significantly outperforms baselines on the content retrieval tasks. Importantly, the MV-HAN is deployed in a real-world matching system. Online A/B test results show that the proposed method can significantly improve the quality of recommendations.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128935980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Assessment of Automated (Intelligent) Toolchains 自动化(智能)工具链的评估

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559572

Aurora Papotti

引用次数: 0

Learning to Synthesize Relational Invariants 学习综合关系不变量

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3556942

Jingbo Wang, Chao Wang

引用次数: 1

Maktub: Lightweight Robot System Test Creation and Automation Maktub:轻型机器人系统测试创建和自动化

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559531

Amr Moussa, J. Ore

引用次数: 0

Test-Driven Multi-Task Learning with Functionally Equivalent Code Transformation for Neural Code Generation 基于功能等效代码转换的神经代码生成测试驱动多任务学习

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3559549

Xin Wang, Xiao Liu, Pingyi Zhou, Qixia Liu, Jin Liu, Hao Wu, Xiao Cui

{"title":"Test-Driven Multi-Task Learning with Functionally Equivalent Code Transformation for Neural Code Generation","authors":"Xin Wang, Xiao Liu, Pingyi Zhou, Qixia Liu, Jin Liu, Hao Wu, Xiao Cui","doi":"10.1145/3551349.3559549","DOIUrl":"https://doi.org/10.1145/3551349.3559549","url":null,"abstract":"Automated code generation is a longstanding challenge in both communities of software engineering and artificial intelligence. Currently, some works have started to investigate the functional correctness of code generation, where a code snippet is considered correct if it passes a set of test cases. However, most existing works still model code generation as text generation without considering program-specific information, such as functionally equivalent code snippets and test execution feedback. To address the above limitations, this paper proposes a method combining program analysis with deep learning for neural code generation, where functionally equivalent code snippets and test execution feedback will be considered at the training stage. Concretely, we firstly design several code transformation heuristics to produce different variants of the code snippet satisfying the same functionality. In addition, we employ the test execution feedback and design a test-driven discriminative task to train a novel discriminator, aiming to let the model distinguish whether the generated code is correct or not. The preliminary results on a newly published dataset demonstrate the effectiveness of our proposed framework for code generation. Particularly, in terms of the pass@1 metric, we achieve 8.81 and 11.53 gains compared with CodeGPT and CodeT5, respectively.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128544736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Learning to Construct Better Mutation Faults 学习构造更好的突变故障

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3556949

Zhao Tian, Junjie Chen, Qihao Zhu, Jie Yang, Lingming Zhang

{"title":"Learning to Construct Better Mutation Faults","authors":"Zhao Tian, Junjie Chen, Qihao Zhu, Jie Yang, Lingming Zhang","doi":"10.1145/3551349.3556949","DOIUrl":"https://doi.org/10.1145/3551349.3556949","url":null,"abstract":"Mutation faults are the core of mutation testing and have been widely used in many other software testing and debugging tasks. Hence, constructing high-quality mutation faults is critical. There are many traditional mutation techniques that construct syntactic mutation faults based on a limited set of manually-defined mutation operators. To improve them, the state-of-the-art deep-learning (DL) based technique (i.e., DeepMutation) has been proposed to construct mutation faults by learning from real faults via classic sequence-to-sequence neural machine translation (NMT). However, its performance is not satisfactory since it cannot ensure syntactic correctness of constructed mutation faults and suffers from the effectiveness issue due to the huge search space and limited features by simply treating each targeted method as a token stream. In this work, we propose a novel DL-based mutation technique (i.e., LEAM) to overcome the limitations of both traditional techniques and DeepMutation. LEAM adapts the syntax-guided encoder-decoder architecture by extending a set of grammar rules specific to our mutation task, to guarantee syntactic correctness of constructed mutation faults. Instead of predicting a sequence of tokens one by one to form a whole mutated method, it predicts the statements to be mutated under the context of the targeted method to reduce search space, and then predicts grammar rules for mutation fault construction based on both semantic and structural features in AST. We conducted an extensive study to evaluate LEAM based on the widely-used Defects4J benchmark. The results demonstrate that the mutation faults constructed by LEAM can not only better represent real faults than two state-of-the-art traditional techniques (i.e., Major and PIT) and DeepMutation, but also substantially boost two important downstream applications of mutation faults, i.e., test case prioritization and fault localization.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124223054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

Learning Contract Invariants Using Reinforcement Learning 使用强化学习学习契约不变量

Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering Pub Date : 2022-10-10 DOI: 10.1145/3551349.3556962

Junrui Liu, Yanju Chen, Bryan Tan, Işıl Dillig, Yu Feng

{"title":"Learning Contract Invariants Using Reinforcement Learning","authors":"Junrui Liu, Yanju Chen, Bryan Tan, Işıl Dillig, Yu Feng","doi":"10.1145/3551349.3556962","DOIUrl":"https://doi.org/10.1145/3551349.3556962","url":null,"abstract":"Due to the popularity of smart contracts in the modern financial ecosystem, there has been growing interest in formally verifying their correctness and security properties. Most existing techniques in this space focus on common vulnerabilities like arithmetic overflows and perform verification by leveraging contract invariants (i.e., logical formulas hold at transaction boundaries). In this paper, we propose a new technique, based on deep reinforcement learning, for automatically learning contract invariants that are useful for proving arithmetic safety. Our method incorporates an off-line training phase in which the verifier uses its own verification attempts to learn a policy for contract invariant generation. This learned (neural) policy is then used at verification time to predict likely invariants that are also useful for proving arithmetic safety. We implemented this idea in a tool called Cider and incorporated it into an existing verifier (based on refinement type checking) for proving arithmetic safety. Our evaluation shows that Cider improves both the quality of the inferred invariants as well as inference time, leading to faster verification and hardened contracts with fewer run-time assertions.","PeriodicalId":197939,"journal":{"name":"Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124577714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3