Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00023
Weigang He, Xia Mao, Ting Su, Yanhong Huang, Jianqi Shi
Programmable logic controllers (PLCs) are broadly used in the safety-critical industrial field, which requires high reliability to avoid catastrophes. Data flow testing (DFT) focuses on data flow relationships in a program and has a stronger fault-detection ability than other control flow-based testing. However, there is no automated testing tool supporting DFT for PLC programs. Hence, we propose an automated data flow testing framework for PLC programs. Our DFT framework is based on dynamic symbolic execution (DSE). Considering the cyclic execution feature of PLC programs, our approach needs reachable states which can be provided by branch testing. Besides, our approach improves testing performance through a novel guided path search algorithm. Furthermore, we evaluate our approach on several programs to demonstrate that this approach is practical and effective.
{"title":"Data Flow Testing for PLC Programs via Dynamic Symbolic Execution","authors":"Weigang He, Xia Mao, Ting Su, Yanhong Huang, Jianqi Shi","doi":"10.1109/APSEC53868.2021.00023","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00023","url":null,"abstract":"Programmable logic controllers (PLCs) are broadly used in the safety-critical industrial field, which requires high reliability to avoid catastrophes. Data flow testing (DFT) focuses on data flow relationships in a program and has a stronger fault-detection ability than other control flow-based testing. However, there is no automated testing tool supporting DFT for PLC programs. Hence, we propose an automated data flow testing framework for PLC programs. Our DFT framework is based on dynamic symbolic execution (DSE). Considering the cyclic execution feature of PLC programs, our approach needs reachable states which can be provided by branch testing. Besides, our approach improves testing performance through a novel guided path search algorithm. Furthermore, we evaluate our approach on several programs to demonstrate that this approach is practical and effective.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126859698","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}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00047
N. B. Lindström, Dina Koutsikouri, M. Staron, Wilhelm Meding, Ola Söder
In this paper, we explore challenges in communication between metrics teams and stakeholders in metrics service delivery. Drawing on interviews and interactive workshops with team members and stakeholders at two different Swedish agile software development organizations, we identify interrelated challenges such as aligning expectations, prioritizing demands, providing regular feedback, and maintaining continuous dialogue, which influence team-stakeholder interaction, relationships and performance. Our study shows the importance of understanding communicative hurdles and provides suggestions for their mitigation, therefore meriting further empirical research.
{"title":"Understanding Metrics Team-Stakeholder Communication in Agile Metrics Service Delivery","authors":"N. B. Lindström, Dina Koutsikouri, M. Staron, Wilhelm Meding, Ola Söder","doi":"10.1109/APSEC53868.2021.00047","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00047","url":null,"abstract":"In this paper, we explore challenges in communication between metrics teams and stakeholders in metrics service delivery. Drawing on interviews and interactive workshops with team members and stakeholders at two different Swedish agile software development organizations, we identify interrelated challenges such as aligning expectations, prioritizing demands, providing regular feedback, and maintaining continuous dialogue, which influence team-stakeholder interaction, relationships and performance. Our study shows the importance of understanding communicative hurdles and provides suggestions for their mitigation, therefore meriting further empirical research.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"249 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122107840","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}
In this paper, we suggest an approach for extracting fine-grained state transition tables using the KLEE symbolic execution engine to assist developers in understanding the behavior of C source code for embedded systems.
{"title":"Extracting a Micro State Transition Table Using the KLEE Symbolic Execution Engine","authors":"Norihiro Yoshida, Takahiro Shimizu, Ryota Yamamoto, Hiroaki Takada","doi":"10.1109/APSEC53868.2021.00072","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00072","url":null,"abstract":"In this paper, we suggest an approach for extracting fine-grained state transition tables using the KLEE symbolic execution engine to assist developers in understanding the behavior of C source code for embedded systems.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128279789","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}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00032
Anas Dakkak, Hongyi Zhang, D. I. Mattos, Jan Bosch, H. H. Olsson
Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While there is no shortage of possible use cases leveraging data from in-service products, software-intensive embedded systems companies struggle to continuously collect data from their in-service products. Often, data collection is done in an ad-hoc way and targeting specific use cases or needs. Besides, few studies have investigated data collection challenges in relation to the data dimensions, which are the minimum set of quantifiable data aspects that can define software-intensive embedded product data from a collection point of view. To help address data collection challenges, and to provide companies with guidance on how to improve this process, we conducted a case study at a large multinational telecommunications supplier focusing on data characteristics and collection challenges from the Radio Access Networks (RAN) products. We further investigated the relations of these challenges to the data dimensions to increase our understanding of how data dominions contribute to the challenges.
{"title":"Towards Continuous Data Collection from In-service Products: Exploring the Relation Between Data Dimensions and Collection Challenges","authors":"Anas Dakkak, Hongyi Zhang, D. I. Mattos, Jan Bosch, H. H. Olsson","doi":"10.1109/APSEC53868.2021.00032","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00032","url":null,"abstract":"Data collected from in-service products play an important role in enabling software-intensive embedded systems suppliers to embrace data-driven practices. Data can be used in many different ways such as to continuously learn and improve the product, enhance post-deployment services, reduce operational cost or create a better user experience. While there is no shortage of possible use cases leveraging data from in-service products, software-intensive embedded systems companies struggle to continuously collect data from their in-service products. Often, data collection is done in an ad-hoc way and targeting specific use cases or needs. Besides, few studies have investigated data collection challenges in relation to the data dimensions, which are the minimum set of quantifiable data aspects that can define software-intensive embedded product data from a collection point of view. To help address data collection challenges, and to provide companies with guidance on how to improve this process, we conducted a case study at a large multinational telecommunications supplier focusing on data characteristics and collection challenges from the Radio Access Networks (RAN) products. We further investigated the relations of these challenges to the data dimensions to increase our understanding of how data dominions contribute to the challenges.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124708410","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}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00028
Yuxuan Li, Ruitao Feng, Sen Chen, Qianyu Guo, Lingling Fan, Xiaohong Li
As a result of the technical evolution in network technologies and the upper applications, the reliance of mobile apps on the Internet increased heavily on the purpose of excellent service in years. However, the speedy increase brought not only conveniences but also security risks. For instance, it is unveiled that there exists a series of malicious apps, which are aiming to collect users’ private data and imperceptibly send them to remote servers under the camouflage of normal users’ behaviors. To defend against the threat, although lots of research has been proposed, it is still a challenge to capture the abnormal behaviors more precisely. In this paper, we propose IconChecker, a GUI-based anomaly detection framework, to detect icons that can cause malicious network payloads under the premise of users’ normal intentions. IconChecker can detect the abnormal icon-behaviors with the icon's semantics and triggered network traffic in relatively high precision, and further generate a security report for analysis and development. To demonstrate the effectiveness, we evaluate IconChecker from: (1) the accuracy of network traffic sniffing; (2) the accuracy of icon semantics classification; (3) the overall precision of IconChecker towards real apps; (4) comparing IconChecker with the existing tool, i.e., DeepIntent. The detection results show that IconChecker can outperform at the precision of 84% in terms of our summarized 8 categories of icon-behaviors. We remark that IconChecker is the first work, which dynamically detects abnormal icon-behaviors, to identify the malicious network payloads in Android apps.
{"title":"IconChecker: Anomaly Detection of Icon-Behaviors for Android Apps","authors":"Yuxuan Li, Ruitao Feng, Sen Chen, Qianyu Guo, Lingling Fan, Xiaohong Li","doi":"10.1109/APSEC53868.2021.00028","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00028","url":null,"abstract":"As a result of the technical evolution in network technologies and the upper applications, the reliance of mobile apps on the Internet increased heavily on the purpose of excellent service in years. However, the speedy increase brought not only conveniences but also security risks. For instance, it is unveiled that there exists a series of malicious apps, which are aiming to collect users’ private data and imperceptibly send them to remote servers under the camouflage of normal users’ behaviors. To defend against the threat, although lots of research has been proposed, it is still a challenge to capture the abnormal behaviors more precisely. In this paper, we propose IconChecker, a GUI-based anomaly detection framework, to detect icons that can cause malicious network payloads under the premise of users’ normal intentions. IconChecker can detect the abnormal icon-behaviors with the icon's semantics and triggered network traffic in relatively high precision, and further generate a security report for analysis and development. To demonstrate the effectiveness, we evaluate IconChecker from: (1) the accuracy of network traffic sniffing; (2) the accuracy of icon semantics classification; (3) the overall precision of IconChecker towards real apps; (4) comparing IconChecker with the existing tool, i.e., DeepIntent. The detection results show that IconChecker can outperform at the precision of 84% in terms of our summarized 8 categories of icon-behaviors. We remark that IconChecker is the first work, which dynamically detects abnormal icon-behaviors, to identify the malicious network payloads in Android apps.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124789427","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}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00008
Aiping Zhang, Kui Liu, Liming Fang, Qianjun Liu, Xinyu Yun, S. Ji
Deep learning techniques have achieved promising results in code clone detection in the past decade. However, existing techniques merely focus on how to extract more dis-criminative features from source codes, while some issues, such as structural differences of functional similar codes, are not explicitly addressed. This phenomenon is common when programmers copy a code segment along with adding or removing several statements, or use a more flexible syntax structure to implement the same function. In this paper, we unify the aforementioned problems as the problem of code misalignment, and propose a novel code alignment network to tackle it. We design a bi-directional causal convolutional neural network to extract feature representations of code fragments with rich structural and semantical information. After feature extraction, our method learns to align the two code fragments in a data-driven fashion. We present two independent strategies for code alignment, namely attention-based alignment and sparse reconstruction-based alignment. Both two strategies strive to learn an alignment matrix that represents the correspondences between two code fragments. Our method outperforms state-of-the-art methods in terms of F1 score by 0.5% and 3.1 % on BigCloneBench and OJClone, respectively11Our code is available at https://github.com/ArcticHare105/Code-Alignment.
{"title":"Learn To Align: A Code Alignment Network For Code Clone Detection","authors":"Aiping Zhang, Kui Liu, Liming Fang, Qianjun Liu, Xinyu Yun, S. Ji","doi":"10.1109/APSEC53868.2021.00008","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00008","url":null,"abstract":"Deep learning techniques have achieved promising results in code clone detection in the past decade. However, existing techniques merely focus on how to extract more dis-criminative features from source codes, while some issues, such as structural differences of functional similar codes, are not explicitly addressed. This phenomenon is common when programmers copy a code segment along with adding or removing several statements, or use a more flexible syntax structure to implement the same function. In this paper, we unify the aforementioned problems as the problem of code misalignment, and propose a novel code alignment network to tackle it. We design a bi-directional causal convolutional neural network to extract feature representations of code fragments with rich structural and semantical information. After feature extraction, our method learns to align the two code fragments in a data-driven fashion. We present two independent strategies for code alignment, namely attention-based alignment and sparse reconstruction-based alignment. Both two strategies strive to learn an alignment matrix that represents the correspondences between two code fragments. Our method outperforms state-of-the-art methods in terms of F1 score by 0.5% and 3.1 % on BigCloneBench and OJClone, respectively11Our code is available at https://github.com/ArcticHare105/Code-Alignment.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114443898","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}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00013
Yu Wang, Huaxiao Liu, Shanquan Gao, Shujia Li
To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.
{"title":"Categorizing npm Packages by Analyzing the Text Information in Software Repositories","authors":"Yu Wang, Huaxiao Liu, Shanquan Gao, Shujia Li","doi":"10.1109/APSEC53868.2021.00013","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00013","url":null,"abstract":"To prevent JavaScript developers from reinventing wheels, npm ecosystem provides numerous third-party libraries for developers to realize relevant functionalities. Npm displays the tags provided by the creators for these packages to help developers find suitable ones. However, not all creators have the habit of tagging their packages, and thus npm cannot provide tag information of a lot of packages for developers to help them understand the package functionalities effectively. Considering that many tags are unrelated to the functionality of packages, we propose a method to find out the tags that are important to distinguish the functionality categories of packages and assign them to untagged packages for assisting developers in the process of retrieving the packages. Firstly, we analyze the attribute of existing tags in npm to establish category tags (functionality categories). Then, we further mine the readme of tagged packages to generate keywords for each category tag. Finally, our method identifies category tags for untagged packages by measuring the similarity between their readme and the keywords of category tags. The evaluation demonstrates that our approach has a good performance in assigning category tags to untagged packages.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527260","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}
Analyzing worst-case resource usage of a program is a difficult but important problem. Existing static bound analysis techniques mainly focus on deriving the upper-bound number of visits to a given control location or iterations of a loop. However, there still exist gaps between such bounds and resource usage bounds. In this paper, we present a static analysis approach to derive resource usage bounds for imperative programs. We leverage techniques of program transformation, numerical value analysis, pointer analysis and program slicing, to model and analyze resource usage in a program. We have conducted experiments to derive usage bounds of various resources in C programs, including heap memory, file descriptors, sockets, user-defined resources, etc. The result suggests that our approach can infer usage bounds of resources in practical imperative programs.
{"title":"Static Analysis of Resource Usage Bounds for Imperative Programs","authors":"Liqian Chen, Taoqing Chen, Guangsheng Fan, Banghu Yin","doi":"10.1109/APSEC53868.2021.00077","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00077","url":null,"abstract":"Analyzing worst-case resource usage of a program is a difficult but important problem. Existing static bound analysis techniques mainly focus on deriving the upper-bound number of visits to a given control location or iterations of a loop. However, there still exist gaps between such bounds and resource usage bounds. In this paper, we present a static analysis approach to derive resource usage bounds for imperative programs. We leverage techniques of program transformation, numerical value analysis, pointer analysis and program slicing, to model and analyze resource usage in a program. We have conducted experiments to derive usage bounds of various resources in C programs, including heap memory, file descriptors, sockets, user-defined resources, etc. The result suggests that our approach can infer usage bounds of resources in practical imperative programs.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122237397","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}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00055
Liming Dong, He Zhang, Lanxin Yang, Zhiluo Weng, Xin Yang, Xin Zhou, Zifan Pan
Despite widespread agreement on the benefits of code review, its outcomes may not be as expected. The complications can undermine the purpose of the development process and even destroy the entire development cycle. Both academia and the industrial communities have invested a great deal of time and effort into code reviews. When a project team adheres to the best practices and creates a conducive environment, it is likely that code reviews could be conducted effectively and efficiently. By reviewing peer-reviewed scientific publications and gray literature on code review best practices, we summarized 57 practices as well as 19 code review pains that they address. Our review has shown that following best practices can ease the process of code review considerably. Multiple actionable practices are needed to support code review pains at the same time. To enable the adoption of best practices, OSS and industrial communities alike invest in integrating automatic techniques with code review tools. We hope that this review will provide researchers and practitioners with a comprehensive understanding of code review practices, aiding them in conducting code reviews more successfully.
{"title":"Survey on Pains and Best Practices of Code Review","authors":"Liming Dong, He Zhang, Lanxin Yang, Zhiluo Weng, Xin Yang, Xin Zhou, Zifan Pan","doi":"10.1109/APSEC53868.2021.00055","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00055","url":null,"abstract":"Despite widespread agreement on the benefits of code review, its outcomes may not be as expected. The complications can undermine the purpose of the development process and even destroy the entire development cycle. Both academia and the industrial communities have invested a great deal of time and effort into code reviews. When a project team adheres to the best practices and creates a conducive environment, it is likely that code reviews could be conducted effectively and efficiently. By reviewing peer-reviewed scientific publications and gray literature on code review best practices, we summarized 57 practices as well as 19 code review pains that they address. Our review has shown that following best practices can ease the process of code review considerably. Multiple actionable practices are needed to support code review pains at the same time. To enable the adoption of best practices, OSS and industrial communities alike invest in integrating automatic techniques with code review tools. We hope that this review will provide researchers and practitioners with a comprehensive understanding of code review practices, aiding them in conducting code reviews more successfully.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"EM-24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121006814","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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