Modern Code review (MCR) techniques are widely adopted in both open-source software platforms and organizations to ensure the quality of their software products. However, the selection of reviewers for code review is cumbersome with the increasing size of development teams. The recommendation of inappropriate reviewers for code review can take more time and effort to complete the task effectively. In this paper, we extended the baseline of reviewers' recommendation framework - RevFinder - to handle issues with newly created files, retired reviewers, the external validity of results, and the accuracies of the state-of-the-art RevFinder. Our proposed hybrid approach, CORMS, works on similarity analysis to compute similarities among file paths, projects/sub-projects, author information, and prediction models to recommend reviewers based on the subject of the change. We conducted a detailed analysis on the widely used 20 projects of both Gerrit and GitHub to compare our results with RevFinder. Our results reveal that on average, CORMS, can achieve top-1, top-3, top-5, and top-10 accuracies, and Mean Reciprocal Rank (MRR) of 45.1%, 67.5%, 74.6%, 79.9% and 0.58 for the 20 projects, consequently improves the RevFinder approach by 44.9%, 34.4%, 20.8%, 12.3% and 18.4%, respectively.
{"title":"CORMS: a GitHub and Gerrit based hybrid code reviewer recommendation approach for modern code review","authors":"Prahar Pandya, Saurabh Tiwari","doi":"10.1145/3540250.3549115","DOIUrl":"https://doi.org/10.1145/3540250.3549115","url":null,"abstract":"Modern Code review (MCR) techniques are widely adopted in both open-source software platforms and organizations to ensure the quality of their software products. However, the selection of reviewers for code review is cumbersome with the increasing size of development teams. The recommendation of inappropriate reviewers for code review can take more time and effort to complete the task effectively. In this paper, we extended the baseline of reviewers' recommendation framework - RevFinder - to handle issues with newly created files, retired reviewers, the external validity of results, and the accuracies of the state-of-the-art RevFinder. Our proposed hybrid approach, CORMS, works on similarity analysis to compute similarities among file paths, projects/sub-projects, author information, and prediction models to recommend reviewers based on the subject of the change. We conducted a detailed analysis on the widely used 20 projects of both Gerrit and GitHub to compare our results with RevFinder. Our results reveal that on average, CORMS, can achieve top-1, top-3, top-5, and top-10 accuracies, and Mean Reciprocal Rank (MRR) of 45.1%, 67.5%, 74.6%, 79.9% and 0.58 for the 20 projects, consequently improves the RevFinder approach by 44.9%, 34.4%, 20.8%, 12.3% and 18.4%, respectively.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"274 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86362064","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}
Chenxi Zhang, Xin Peng, Tong Zhou, Chaofeng Sha, Zhenghui Yan, Yiru Chen, Hong Yang
Due to the large amount and high complexity of trace data, microservice trace analysis tasks such as anomaly detection, fault diagnosis, and tail-based sampling widely adopt machine learning technology. These trace analysis approaches usually use a preprocessing step to map structured features of traces to vector representations in an ad-hoc way. Therefore, they may lose important information such as topological dependencies between service operations. In this paper, we propose TraceCRL, a trace representation learning approach based on contrastive learning and graph neural network, which can incorporate graph structured information in the downstream trace analysis tasks. Given a trace, TraceCRL constructs an operation invocation graph where nodes represent service operations and edges represent operation invocations together with predefined features for invocation status and related metrics. Based on the operation invocation graphs of traces TraceCRL uses a contrastive learning method to train a graph neural network-based model for trace representation. In particular, TraceCRL employs six trace data augmentation strategies to alleviate the problems of class collision and uniformity of representation in contrastive learning. Our experimental studies show that TraceCRL can significantly improve the performance of trace anomaly detection and offline trace sampling. It also confirms the effectiveness of the trace augmentation strategies and the efficiency of TraceCRL.
{"title":"TraceCRL: contrastive representation learning for microservice trace analysis","authors":"Chenxi Zhang, Xin Peng, Tong Zhou, Chaofeng Sha, Zhenghui Yan, Yiru Chen, Hong Yang","doi":"10.1145/3540250.3549146","DOIUrl":"https://doi.org/10.1145/3540250.3549146","url":null,"abstract":"Due to the large amount and high complexity of trace data, microservice trace analysis tasks such as anomaly detection, fault diagnosis, and tail-based sampling widely adopt machine learning technology. These trace analysis approaches usually use a preprocessing step to map structured features of traces to vector representations in an ad-hoc way. Therefore, they may lose important information such as topological dependencies between service operations. In this paper, we propose TraceCRL, a trace representation learning approach based on contrastive learning and graph neural network, which can incorporate graph structured information in the downstream trace analysis tasks. Given a trace, TraceCRL constructs an operation invocation graph where nodes represent service operations and edges represent operation invocations together with predefined features for invocation status and related metrics. Based on the operation invocation graphs of traces TraceCRL uses a contrastive learning method to train a graph neural network-based model for trace representation. In particular, TraceCRL employs six trace data augmentation strategies to alleviate the problems of class collision and uniformity of representation in contrastive learning. Our experimental studies show that TraceCRL can significantly improve the performance of trace anomaly detection and offline trace sampling. It also confirms the effectiveness of the trace augmentation strategies and the efficiency of TraceCRL.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88475779","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}
Shengyi Pan, Jiayuan Zhou, F. R. Côgo, Xin Xia, Lingfeng Bao, Xing Hu, Shanping Li, Ahmed E. Hassan
The coordinated vulnerability disclosure (CVD) process is commonly adopted for open source software (OSS) vulnerability management, which suggests to privately report the discovered vulnerabilities and keep relevant information secret until the official disclosure. However, in practice, due to various reasons (e.g., lacking security domain expertise or the sense of security management), many vulnerabilities are first reported via public issue reports (IRs) before its official disclosure. Such IRs are dangerous IRs, since attackers can take advantages of the leaked vulnerability information to launch zero-day attacks. It is crucial to identify such dangerous IRs at an early stage, such that OSS users can start the vulnerability remediation process earlier and OSS maintainers can timely manage the dangerous IRs. In this paper, we propose and evaluate a deep learning based approach, namely MemVul, to automatically identify dangerous IRs at the time they are reported. MemVul augments the neural networks with a memory component, which stores the external vulnerability knowledge from Common Weakness Enumeration (CWE). We rely on publicly accessible CVE-referred IRs (CIRs) to operationalize the concept of dangerous IR. We mine 3,937 CIRs distributed across 1,390 OSS projects hosted on GitHub. Evaluated under a practical scenario of high data imbalance, MemVul achieves the best trade-off between precision and recall among all baselines. In particular, the F1-score of MemVul (i.e., 0.49) improves the best performing baseline by 44%. For IRs that are predicted as CIRs but not reported to CVE, we conduct a user study to investigate their usefulness to OSS stakeholders. We observe that 82% (41 out of 50) of these IRs are security-related and 28 of them are suggested by security experts to be publicly disclosed, indicating MemVul is capable of identifying undisclosed dangerous IRs.
{"title":"Automated unearthing of dangerous issue reports","authors":"Shengyi Pan, Jiayuan Zhou, F. R. Côgo, Xin Xia, Lingfeng Bao, Xing Hu, Shanping Li, Ahmed E. Hassan","doi":"10.1145/3540250.3549156","DOIUrl":"https://doi.org/10.1145/3540250.3549156","url":null,"abstract":"The coordinated vulnerability disclosure (CVD) process is commonly adopted for open source software (OSS) vulnerability management, which suggests to privately report the discovered vulnerabilities and keep relevant information secret until the official disclosure. However, in practice, due to various reasons (e.g., lacking security domain expertise or the sense of security management), many vulnerabilities are first reported via public issue reports (IRs) before its official disclosure. Such IRs are dangerous IRs, since attackers can take advantages of the leaked vulnerability information to launch zero-day attacks. It is crucial to identify such dangerous IRs at an early stage, such that OSS users can start the vulnerability remediation process earlier and OSS maintainers can timely manage the dangerous IRs. In this paper, we propose and evaluate a deep learning based approach, namely MemVul, to automatically identify dangerous IRs at the time they are reported. MemVul augments the neural networks with a memory component, which stores the external vulnerability knowledge from Common Weakness Enumeration (CWE). We rely on publicly accessible CVE-referred IRs (CIRs) to operationalize the concept of dangerous IR. We mine 3,937 CIRs distributed across 1,390 OSS projects hosted on GitHub. Evaluated under a practical scenario of high data imbalance, MemVul achieves the best trade-off between precision and recall among all baselines. In particular, the F1-score of MemVul (i.e., 0.49) improves the best performing baseline by 44%. For IRs that are predicted as CIRs but not reported to CVE, we conduct a user study to investigate their usefulness to OSS stakeholders. We observe that 82% (41 out of 50) of these IRs are security-related and 28 of them are suggested by security experts to be publicly disclosed, indicating MemVul is capable of identifying undisclosed dangerous IRs.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90711624","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}
X. Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li
Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing well-established benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study of non-termination bugs in real-world OSS projects. Specifically, we first devoted substantial manual efforts in collecting and analyzing 445 non-termination bugs from 3,142 GitHub commits and provided a systematic classifi-cation of the bugs based on their root causes. We constructed a new benchmark set characterizing the real-world bugs with simplified programs, including a non-termination dataset with 56 real and reproducible non-termination bugs and a termination dataset with 58 fixed programs. With the constructed benchmark, we evaluated five state-of-the-art termination analysis tools. The results show that the capabilities of the tested tools to make correct verdicts have obviously dropped compared with the existing benchmarks. Meanwhile, we identified the challenges and limitations that these tools face by analyzing the root causes of their unhandled bugs. Fi-nally, we summarized the challenges and future research directions for detecting non-termination bugs in real-world projects.
{"title":"Large-scale analysis of non-termination bugs in real-world OSS projects","authors":"X. Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li","doi":"10.1145/3540250.3549129","DOIUrl":"https://doi.org/10.1145/3540250.3549129","url":null,"abstract":"Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing well-established benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study of non-termination bugs in real-world OSS projects. Specifically, we first devoted substantial manual efforts in collecting and analyzing 445 non-termination bugs from 3,142 GitHub commits and provided a systematic classifi-cation of the bugs based on their root causes. We constructed a new benchmark set characterizing the real-world bugs with simplified programs, including a non-termination dataset with 56 real and reproducible non-termination bugs and a termination dataset with 58 fixed programs. With the constructed benchmark, we evaluated five state-of-the-art termination analysis tools. The results show that the capabilities of the tested tools to make correct verdicts have obviously dropped compared with the existing benchmarks. Meanwhile, we identified the challenges and limitations that these tools face by analyzing the root causes of their unhandled bugs. Fi-nally, we summarized the challenges and future research directions for detecting non-termination bugs in real-world projects.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88550106","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}
Thomas Laurent, Paolo Arcaini, Catia Trubiani, Anthony Ventresque
Understanding the performance characteristics of software systems is particular relevant when looking at design alternatives. However, it is a very challenging problem, due to the complexity of interpreting the role and incidence of the different system elements on performance metrics of interest, such as system response time or resources utilisation. This work introduces JSIMutate, a tool that makes use of queueing network performance models and enables the analysis of mutations of a model reflecting possible design changes to support designers in identifying the model elements that contribute to improving or worsening the system's performance.
{"title":"JSIMutate: understanding performance results through mutations","authors":"Thomas Laurent, Paolo Arcaini, Catia Trubiani, Anthony Ventresque","doi":"10.1145/3540250.3558930","DOIUrl":"https://doi.org/10.1145/3540250.3558930","url":null,"abstract":"Understanding the performance characteristics of software systems is particular relevant when looking at design alternatives. However, it is a very challenging problem, due to the complexity of interpreting the role and incidence of the different system elements on performance metrics of interest, such as system response time or resources utilisation. This work introduces JSIMutate, a tool that makes use of queueing network performance models and enables the analysis of mutations of a model reflecting possible design changes to support designers in identifying the model elements that contribute to improving or worsening the system's performance.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"133 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86394281","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}
Yucen Shi, Ying Yin, Zhengkui Wang, David Lo, Tao Zhang, Xin Xia, Yuhai Zhao, Bowen Xu
Semantic code search greatly facilitates software reuse, which enables users to find code snippets highly matching user-specified natural language queries. Due to the rich expressive power of code graphs (e.g., control-flow graph and program dependency graph), both of the two mainstream research works (i.e., multi-modal models and pre-trained models) have attempted to incorporate code graphs for code modelling. However, they still have some limitations: First, there is still much room for improvement in terms of search effectiveness. Second, they have not fully considered the unique features of code graphs. In this paper, we propose a Graph-to-Sequence Converter, namely G2SC. Through converting the code graphs into lossless sequences, G2SC enables to address the problem of small graph learning using sequence feature learning and capture both the edges and nodes attribute information of code graphs. Thus, the effectiveness of code search can be greatly improved. In particular, G2SC first converts the code graph into a unique corresponding node sequence by a specific graph traversal strategy. Then, it gets a statement sequence by replacing each node with its corresponding statement. A set of carefully designed graph traversal strategies guarantee that the process is one-to-one and reversible. G2SC enables capturing rich semantic relationships (i.e., control flow, data flow, node/relationship properties) and provides learning model-friendly data transformation. It can be flexibly integrated with existing models to better utilize the code graphs. As a proof-of-concept application, we present two G2SC enabled models: GSMM (G2SC enabled multi-modal model) and GSCodeBERT (G2SC enabled CodeBERT model). Extensive experiment results on two real large-scale datasets demonstrate that GSMM and GSCodeBERT can greatly improve the state-of-the-art models MMAN and GraphCodeBERT by 92% and 22% on R@1, and 63% and 11.5% on MRR, respectively.
{"title":"How to better utilize code graphs in semantic code search?","authors":"Yucen Shi, Ying Yin, Zhengkui Wang, David Lo, Tao Zhang, Xin Xia, Yuhai Zhao, Bowen Xu","doi":"10.1145/3540250.3549087","DOIUrl":"https://doi.org/10.1145/3540250.3549087","url":null,"abstract":"Semantic code search greatly facilitates software reuse, which enables users to find code snippets highly matching user-specified natural language queries. Due to the rich expressive power of code graphs (e.g., control-flow graph and program dependency graph), both of the two mainstream research works (i.e., multi-modal models and pre-trained models) have attempted to incorporate code graphs for code modelling. However, they still have some limitations: First, there is still much room for improvement in terms of search effectiveness. Second, they have not fully considered the unique features of code graphs. In this paper, we propose a Graph-to-Sequence Converter, namely G2SC. Through converting the code graphs into lossless sequences, G2SC enables to address the problem of small graph learning using sequence feature learning and capture both the edges and nodes attribute information of code graphs. Thus, the effectiveness of code search can be greatly improved. In particular, G2SC first converts the code graph into a unique corresponding node sequence by a specific graph traversal strategy. Then, it gets a statement sequence by replacing each node with its corresponding statement. A set of carefully designed graph traversal strategies guarantee that the process is one-to-one and reversible. G2SC enables capturing rich semantic relationships (i.e., control flow, data flow, node/relationship properties) and provides learning model-friendly data transformation. It can be flexibly integrated with existing models to better utilize the code graphs. As a proof-of-concept application, we present two G2SC enabled models: GSMM (G2SC enabled multi-modal model) and GSCodeBERT (G2SC enabled CodeBERT model). Extensive experiment results on two real large-scale datasets demonstrate that GSMM and GSCodeBERT can greatly improve the state-of-the-art models MMAN and GraphCodeBERT by 92% and 22% on R@1, and 63% and 11.5% on MRR, respectively.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81342607","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}
O. Vašíček, Jan Fiedor, Tomas Kratochvila, B. Krena, A. Smrčka, Tomáš Vojnar
This paper describes Unite, a new tool intended as an adapter for transforming non-interactive command-line analysis tools to OSLC-compliant web services. Unite aims to make such tools easier to adopt and more convenient to use by allowing them to be accessible, both locally and remotely, in a unified way and to be easily integrated into various development environments. Open Services for Lifecycle Collaboration (OSLC) is an open standard for tool integration and was chosen for this task due to its robustness, extensibility, support of data from various domains, and its growing popularity. The work is motivated by allowing existing analysis tools to be more widely used with a strong emphasis on widening their industrial usage. We have implemented Unite and used it with multiple existing static as well as dynamic analysis and verification tools, and then successfully deployed it internationally in the industry to automate verification tasks for development teams in Honeywell. We discuss Honeywell's experience with using Unite and with OSLC in general. Moreover, we also provide the Unite Client (UniC) for Eclipse to allow users to easily run various analysis tools directly from the Eclipse IDE.
{"title":"Unite: an adapter for transforming analysis tools to web services via OSLC","authors":"O. Vašíček, Jan Fiedor, Tomas Kratochvila, B. Krena, A. Smrčka, Tomáš Vojnar","doi":"10.1145/3540250.3558939","DOIUrl":"https://doi.org/10.1145/3540250.3558939","url":null,"abstract":"This paper describes Unite, a new tool intended as an adapter for transforming non-interactive command-line analysis tools to OSLC-compliant web services. Unite aims to make such tools easier to adopt and more convenient to use by allowing them to be accessible, both locally and remotely, in a unified way and to be easily integrated into various development environments. Open Services for Lifecycle Collaboration (OSLC) is an open standard for tool integration and was chosen for this task due to its robustness, extensibility, support of data from various domains, and its growing popularity. The work is motivated by allowing existing analysis tools to be more widely used with a strong emphasis on widening their industrial usage. We have implemented Unite and used it with multiple existing static as well as dynamic analysis and verification tools, and then successfully deployed it internationally in the industry to automate verification tasks for development teams in Honeywell. We discuss Honeywell's experience with using Unite and with OSLC in general. Moreover, we also provide the Unite Client (UniC) for Eclipse to allow users to easily run various analysis tools directly from the Eclipse IDE.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81349700","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}
For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The growing reliance of users with disability on mobile apps to complete their day-to-day tasks further stresses the need for accessible software. Mobile operating systems, such as iOS and Android, provide various integrated assistive services to help individuals with disabilities perform tasks that could otherwise be difficult or not possible. However, for these assistive services to work correctly, developers have to support them in their app by following a set of best practices and accessibility guidelines. Text Scaling Assistive Service (TSAS) is utilized by people with low vision, to increase the text size and make apps accessible to them. However, the use of TSAS with incompatible apps can result in unexpected behavior introducing accessibility barriers to users. This paper presents approach, an automated testing technique for text accessibility issues arising from incompatibility between apps and TSAS. As a first step, we identify five different types of text accessibility by analyzing more than 600 candidate issues reported by users in (i) app reviews for Android and iOS, and (ii) Twitter data collected from public Twitter accounts. To automatically detect such issues, approach utilizes the UI screenshots and various metadata information extracted using dynamic analysis, and then applies various heuristics informed by the different types of text accessibility issues identified earlier. Evaluation of approach on 30 real-world Android apps corroborates its effectiveness by achieving 88.27% precision and 95.76% recall on average in detecting text accessibility issues.
{"title":"AccessiText: automated detection of text accessibility issues in Android apps","authors":"Abdulaziz Alshayban, S. Malek","doi":"10.1145/3540250.3549118","DOIUrl":"https://doi.org/10.1145/3540250.3549118","url":null,"abstract":"For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The growing reliance of users with disability on mobile apps to complete their day-to-day tasks further stresses the need for accessible software. Mobile operating systems, such as iOS and Android, provide various integrated assistive services to help individuals with disabilities perform tasks that could otherwise be difficult or not possible. However, for these assistive services to work correctly, developers have to support them in their app by following a set of best practices and accessibility guidelines. Text Scaling Assistive Service (TSAS) is utilized by people with low vision, to increase the text size and make apps accessible to them. However, the use of TSAS with incompatible apps can result in unexpected behavior introducing accessibility barriers to users. This paper presents approach, an automated testing technique for text accessibility issues arising from incompatibility between apps and TSAS. As a first step, we identify five different types of text accessibility by analyzing more than 600 candidate issues reported by users in (i) app reviews for Android and iOS, and (ii) Twitter data collected from public Twitter accounts. To automatically detect such issues, approach utilizes the UI screenshots and various metadata information extracted using dynamic analysis, and then applies various heuristics informed by the different types of text accessibility issues identified earlier. Evaluation of approach on 30 real-world Android apps corroborates its effectiveness by achieving 88.27% precision and 95.76% recall on average in detecting text accessibility issues.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81829143","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}
Yuandao Cai, Chengfeng Ye, Qingkai Shi, Charles Zhang
Deadlocks still severely inflict reliability and security issues upon software systems of the modern age. Worse still, as we note, in prior static deadlock detectors, good precision does not go hand-in-hand with high scalability --- their approaches are either context-insensitive, thereby engendering many false positives, or suffer from the calling context explosion to reach context-sensitive, thus compromising good efficiency. In this paper, we advocate Peahen, geared towards precise yet also scalable static deadlock detection. At its crux, Peahen decomposes the computational effort for embracing high precision into two cooperative analysis stages: (i) context-insensitive lock-graph construction, which selectively encodes the essential lock-acquisition information on each edge, and (ii) three precise yet lazy refinements, which incorporate such edge information into progressively refining the deadlock cycles in the lock graph only for a few interesting calling contexts. Our extensive experiments yield promising results: Peahen dramatically out-performs the state-of-the-art tools on accuracy without losing scalability; it can efficiently check million-line systems at a low false positive rate; and it has uncovered many confirmed deadlocks in dozens of mature open-source systems.
{"title":"Peahen: fast and precise static deadlock detection via context reduction","authors":"Yuandao Cai, Chengfeng Ye, Qingkai Shi, Charles Zhang","doi":"10.1145/3540250.3549110","DOIUrl":"https://doi.org/10.1145/3540250.3549110","url":null,"abstract":"Deadlocks still severely inflict reliability and security issues upon software systems of the modern age. Worse still, as we note, in prior static deadlock detectors, good precision does not go hand-in-hand with high scalability --- their approaches are either context-insensitive, thereby engendering many false positives, or suffer from the calling context explosion to reach context-sensitive, thus compromising good efficiency. In this paper, we advocate Peahen, geared towards precise yet also scalable static deadlock detection. At its crux, Peahen decomposes the computational effort for embracing high precision into two cooperative analysis stages: (i) context-insensitive lock-graph construction, which selectively encodes the essential lock-acquisition information on each edge, and (ii) three precise yet lazy refinements, which incorporate such edge information into progressively refining the deadlock cycles in the lock graph only for a few interesting calling contexts. Our extensive experiments yield promising results: Peahen dramatically out-performs the state-of-the-art tools on accuracy without losing scalability; it can efficiently check million-line systems at a low false positive rate; and it has uncovered many confirmed deadlocks in dozens of mature open-source systems.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89369826","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}
Software systems increasingly expose performance-sensitive configuration parameters, or PerfConfs, to users. Unfortunately, the right settings of these PerfConfs are difficult to decide and often change at run time. To address this problem, prior research has proposed self-adaptive frameworks that automatically monitor the software’s behavior and dynamically tune configurations to provide the desired performance despite dynamic changes. However, these frameworks often require configuration themselves; sometimes explicitly in the form of additional parameters, sometimes implicitly in the form of training. This paper proposes a new framework, AgileCtrl, that eliminates the need of configuration for a large family of control-based self-adaptive frameworks. AgileCtrl’s key insight is to not just monitor the original software, but additionally to monitor its adaptations and reconfigure itself when its internal adaptation mechanisms are not meeting software requirements. We evaluate AgileCtrl by comparing against recent control-based approaches to self-adaptation that require user configuration. Across a number of case studies, we find AgileCtrl withstands model errors up to 106×, saves the system from performance oscillation and crashes, and improves the performance up to 53%. It also auto-adjusts improper performance goals while improving the performance by 50%.
{"title":"AgileCtrl: a self-adaptive framework for configuration tuning","authors":"Shu Wang, Henry Hoffmann, Shan Lu","doi":"10.1145/3540250.3549136","DOIUrl":"https://doi.org/10.1145/3540250.3549136","url":null,"abstract":"Software systems increasingly expose performance-sensitive configuration parameters, or PerfConfs, to users. Unfortunately, the right settings of these PerfConfs are difficult to decide and often change at run time. To address this problem, prior research has proposed self-adaptive frameworks that automatically monitor the software’s behavior and dynamically tune configurations to provide the desired performance despite dynamic changes. However, these frameworks often require configuration themselves; sometimes explicitly in the form of additional parameters, sometimes implicitly in the form of training. This paper proposes a new framework, AgileCtrl, that eliminates the need of configuration for a large family of control-based self-adaptive frameworks. AgileCtrl’s key insight is to not just monitor the original software, but additionally to monitor its adaptations and reconfigure itself when its internal adaptation mechanisms are not meeting software requirements. We evaluate AgileCtrl by comparing against recent control-based approaches to self-adaptation that require user configuration. Across a number of case studies, we find AgileCtrl withstands model errors up to 106×, saves the system from performance oscillation and crashes, and improves the performance up to 53%. It also auto-adjusts improper performance goals while improving the performance by 50%.","PeriodicalId":68155,"journal":{"name":"软件产业与工程","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87487617","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}
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