2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)最新文献_第6页

Verified from Scratch: Program Analysis for Learners' Programs 从零开始验证:学习者程序的程序分析

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416554

Andreas Stahlbauer, Christoph Frädrich, G. Fraser

{"title":"Verified from Scratch: Program Analysis for Learners' Programs","authors":"Andreas Stahlbauer, Christoph Frädrich, G. Fraser","doi":"10.1145/3324884.3416554","DOIUrl":"https://doi.org/10.1145/3324884.3416554","url":null,"abstract":"Block-based programming languages like Scratch support learners by providing high-level constructs that hide details and by preventing syntactically incorrect programs. Questions nevertheless frequently arise: Is this program satisfying the given task? Why is my program not working? To support learners and educators, automated program analysis is needed for answering such questions. While adapting existing analyses to process blocks instead of textual statements is straightforward, the domain of programs controlled by block-based languages like Scratch is very different from traditional programs: In Scratch multiple actors, represented as highly concurrent programs, interact on a graphical stage, controlled by user inputs, and while the block-based program statements look playful, they hide complex mathematical operations that determine visual aspects and movement. Analyzing such programs is further hampered by the absence of clearly defined semantics, often resulting from ad-hoc decisions made by the implementers of the programming environment. To enable program analysis, we define the semantics of Scratch using an intermediate language. Based on this intermediate language, we implement the Bastet program analysis framework for Scratch programs, using concepts from abstract interpretation and software model checking. Like Scratch, Bastet is based on Web technologies, written in TypeScript, and can be executed using NodeJS or even directly in a browser. Evaluation on 279 programs written by children suggests that Bastet offers a practical solution for analysis of Scratch programs, thus enabling applications such as automated hint generation, automated evaluation of learner progress, or automated grading.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"395 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202100","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: 9

Efficient Multiplex Symbolic Execution with Adaptive Search Strategy 基于自适应搜索策略的高效多路符号执行

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418902

Tianqi Zhang, Yufeng Zhang, Zhenbang Chen, Ziqi Shuai, Ji Wang

引用次数: 3

Multi-task Learning based Pre-trained Language Model for Code Completion 基于多任务学习的预训练语言代码完成模型

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416591

F. Liu, Ge Li, Yunfei Zhao, Zhi Jin

{"title":"Multi-task Learning based Pre-trained Language Model for Code Completion","authors":"F. Liu, Ge Li, Yunfei Zhao, Zhi Jin","doi":"10.1145/3324884.3416591","DOIUrl":"https://doi.org/10.1145/3324884.3416591","url":null,"abstract":"Code completion is one of the most useful features in the Integrated Development Environments (IDEs), which can accelerate software development by suggesting the next probable token based on the contextual code in real-time. Recent studies have shown that statistical language modeling techniques can improve the performance of code completion tools through learning from large-scale software repositories. However, these models suffer from two major drawbacks: a) Existing research uses static embeddings, which map a word to the same vector regardless of its context. The differences in the meaning of a token in varying contexts are lost when each token is associated with a single representation; b) Existing language model based code completion models perform poor on completing identifiers, and the type information of the identifiers is ignored in most of these models. To address these challenges, in this paper, we develop a multi-task learning based pre-trained language model for code understanding and code generation with a Transformer-based neural architecture. We pre-train it with hybrid objective functions that incorporate both code understanding and code generation tasks. Then we fine-tune the pre-trained model on code completion. During the completion, our model does not directly predict the next token. Instead, we adopt multi-task learning to predict the token and its type jointly and utilize the predicted type to assist the token prediction. Experiments results on two real-world datasets demonstrate the effectiveness of our model when compared with state-of-the-art methods.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130123351","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: 126

Towards Immersive Comprehension of Software Systems Using Augmented Reality - An Empirical Evaluation 面向沉浸式理解软件系统使用增强现实-一个实证评估

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418907

Rohit Mehra, V. Sharma, Vikrant S. Kaulgud, Sanjay Podder, Adam P. Burden

{"title":"Towards Immersive Comprehension of Software Systems Using Augmented Reality - An Empirical Evaluation","authors":"Rohit Mehra, V. Sharma, Vikrant S. Kaulgud, Sanjay Podder, Adam P. Burden","doi":"10.1145/3324884.3418907","DOIUrl":"https://doi.org/10.1145/3324884.3418907","url":null,"abstract":"While traditionally, software comprehension relies on approaches like reading through the code or looking at charts on screens, which are 2D mediums, there have been some recent approaches that advocate exploring 3D approaches like Augmented or Virtual Reality (AR/VR) to have a richer experience towards understanding software and its internal relationships. However, there is a dearth of objective studies that compare such 3D representations with their traditional 2D counterparts in the context of software comprehension. In this paper, we present an evaluation study to quantitatively and qualitatively compare 2D and 3D software representations with respect to typical comprehension tasks. For the 3D medium, we utilize an AR-based approach for 3D visualizations of a software system (XRaSE), while the 2D medium comprises of textual IDEs and 2D graph representations. The study, which has been conducted using 20 professional developers, shows that for most comprehension tasks, the developers perform much better using the 3D representation, especially in terms of velocity and recollection, while also displaying reduced cognitive load and better engagement.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116440636","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: 4

A Hybrid Analysis to Detect Java Serialisation Vulnerabilities 检测Java序列化漏洞的混合分析

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418931

Shawn Rasheed, Jens Dietrich

引用次数: 10

On Benign Features in Malware Detection 论恶意软件检测中的良性特征

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418926

Michael Cao, Sahar Badihi, Khaled Ahmed, Peiyu Xiong, J. Rubin

引用次数: 10

Subdomain-Based Generality-Aware Debloating 基于子域的通用性意识膨胀

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416644

Qi Xin, Myeongsoo Kim, Qirun Zhang, A. Orso

{"title":"Subdomain-Based Generality-Aware Debloating","authors":"Qi Xin, Myeongsoo Kim, Qirun Zhang, A. Orso","doi":"10.1145/3324884.3416644","DOIUrl":"https://doi.org/10.1145/3324884.3416644","url":null,"abstract":"Programs are becoming increasingly complex and typically contain an abundance of unneeded features, which can degrade the performance and security of the software. Recently, we have witnessed a surge of debloating techniques that aim to create a reduced version of a program by eliminating the unneeded features therein. To de-bloat a program, most existing techniques require a usage profile of the program, typically provided as a set of inputs $I$. Unfortunately, these techniques tend to generate a reduced program that is overfitted to $I$ and thus fails to behave correctly for other inputs. To address this limitation, we propose Domgad, which has two main advantages over existing debloating approaches. First, it produces a reduced program that is guaranteed to work for subdomains, rather than for specific inputs. Second, it uses stochastic optimization to generate reduced programs that achieve a close-to-optimal tradeoff between reduction and generality (i.e., the extent to which the reduced program is able to correctly handle inputs in its whole domain). To assess the effectiveness of Domgad, we applied our approach to a benchmark of ten Unix utility programs. Our results are promising, as they show that Domgad could produce debloated programs that achieve, on average, 50% code reduction and 95% generality. Our results also show that Domgad performs well when compared with two state-of-the-art debloating approaches.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127325318","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: 8

Managing App Testing Device Clouds: Issues and Opportunities 管理应用测试设备云:问题与机遇

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3418909

M. Fazzini, A. Orso

引用次数: 0

API-Misuse Detection Driven by Fine-Grained API-Constraint Knowledge Graph 基于细粒度api约束知识图的api误用检测

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3416551

Xiaoxue Ren, Xinyuan Ye, Zhenchang Xing, Xin Xia, Xiwei Xu, Liming Zhu, Jianling Sun

{"title":"API-Misuse Detection Driven by Fine-Grained API-Constraint Knowledge Graph","authors":"Xiaoxue Ren, Xinyuan Ye, Zhenchang Xing, Xin Xia, Xiwei Xu, Liming Zhu, Jianling Sun","doi":"10.1145/3324884.3416551","DOIUrl":"https://doi.org/10.1145/3324884.3416551","url":null,"abstract":"API misuses cause significant problem in software development. Existing methods detect API misuses against frequent API usage patterns mined from codebase. They make a naive assumption that API usage that deviates from the most-frequent API usage is a misuse. However, there is a big knowledge gap between API usage patterns and API usage caveats in terms of comprehensiveness, explainability and best practices. In this work, we propose a novel approach that detects API misuses directly against the API caveat knowledge, rather than API usage patterns. We develop open information extraction methods to construct a novel API-constraint knowledge graph from API reference documentation. This knowledge graph explicitly models two types of API-constraint relations (call-order and condition-checking) and enriches return and throw relations with return conditions and exception triggers. It empowers the detection of three types of frequent API misuses - missing calls, missing condition checking and missing exception handling, while existing detectors mostly focus on only missing calls. As a proof-of-concept, we apply our approach to Java SDK API Specification. Our evaluation confirms the high accuracy of the extracted API-constraint relations. Our knowledge-driven API misuse detector achieves 0.60 (68/113) precision and 0.28 (68/239) recall for detecting Java API misuses in the API misuse benchmark MuBench. This performance is significantly higher than that of existing pattern-based API misused detectors. A pilot user study with 12 developers shows that our knowledge-driven API misuse detection is very promising in helping developers avoid API misuses and debug the bugs caused by API misuses.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132110214","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: 36

Industry Practice of JavaScript Dynamic Analysis on WeChat Mini-Programs 微信小程序JavaScript动态分析的行业实践

2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) Pub Date : 2020-09-01 DOI: 10.1145/3324884.3421842

Yi Liu, Jinhui Xie, Jianbo Yang, Shi-ze Guo, Yuetang Deng, Shuqing Li, Yechang Wu, Yepang Liu

{"title":"Industry Practice of JavaScript Dynamic Analysis on WeChat Mini-Programs","authors":"Yi Liu, Jinhui Xie, Jianbo Yang, Shi-ze Guo, Yuetang Deng, Shuqing Li, Yechang Wu, Yepang Liu","doi":"10.1145/3324884.3421842","DOIUrl":"https://doi.org/10.1145/3324884.3421842","url":null,"abstract":"JavaScript is one of the most popular programming languages. WeChat Mini-Program is a large ecosystem of JavaScript applications that runs on the WeChat platform. Millions of Mini-Programs are accessed by WeChat users every week. Consequently, the performance and robustness of Mini-Programs are particularly important. Unfortunately, many Mini-Programs suffer from various defects and performance problems. Dynamic analysis is a useful technique to pinpoint application defects. However, due to the dynamic features of the JavaScript language and the complexity of the runtime environment, dynamic analysis techniques were rarely used to improve the quality of JavaScript applications running on industrial platforms such as WeChat Mini-Program previously. In this work, we report our experience of extending Jalangi, a dynamic analysis framework for JavaScript applications developed by academia, and applying the extended version, named WeJalangi, to diagnose defects in WeChat Mini-Programs. WeJalangi is compatible with existing dynamic analysis tools such as DLint, Smemory, and JITProf. We implemented a null pointer checker on WeJalangi and tested the tool's usability on 152 open-source Mini-Programs. We also conducted a case study in Tencent by applying WeJalangi on six popular commercial Mini-Programs. In the case study, WeJalangi accurately located six null pointer issues and three of them haven't been discovered previously. All of the reported defects have been confirmed by developers and testers.","PeriodicalId":106337,"journal":{"name":"2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE)","volume":"42 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134357185","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: 11