Pub Date : 2019-12-01DOI: 10.1109/APSEC48747.2019.00066
Mohd Hafeez Osman, Stefan Kugele, S. Shafaei
Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive electric and electronic architecture in the future. However, while such systems provide highly advanced functions, safety risk increases as AI-based systems may produce uncertain output and behaviour. In this paper, we devise a run-time safety monitoring framework for AI-based intelligence systems focusing on autonomous driving functions. In detail, this paper describes (i) the characteristics of a safety monitoring framework; (ii) the safety monitoring framework itself, and (iii) we develop a prototype and implement the framework for two critical driving functions: Lane detection and object detection. Through an implementation of the framework to a prototypic control environment, we show the possibility of this framework in the real context. Finally, we discuss the techniques used in developing the safety monitoring framework and describes the encountered challenges.
{"title":"Run-Time Safety Monitoring Framework for AI-Based Systems: Automated Driving Cases","authors":"Mohd Hafeez Osman, Stefan Kugele, S. Shafaei","doi":"10.1109/APSEC48747.2019.00066","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00066","url":null,"abstract":"Intelligent systems based on artificial intelligence techniques are increasing and are recently being accepted in the automotive domain. In the competition of automobile makers to provide fully automated vehicles, it is perceived that artificial intelligence will profoundly influence the automotive electric and electronic architecture in the future. However, while such systems provide highly advanced functions, safety risk increases as AI-based systems may produce uncertain output and behaviour. In this paper, we devise a run-time safety monitoring framework for AI-based intelligence systems focusing on autonomous driving functions. In detail, this paper describes (i) the characteristics of a safety monitoring framework; (ii) the safety monitoring framework itself, and (iii) we develop a prototype and implement the framework for two critical driving functions: Lane detection and object detection. Through an implementation of the framework to a prototypic control environment, we show the possibility of this framework in the real context. Finally, we discuss the techniques used in developing the safety monitoring framework and describes the encountered challenges.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123811848","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}
Formal method is a promising solution for precise software requirements modeling and V&V (Validation and Verification). However, domain experts are suffering from using complex mathematics formal notations to precisely describe their domain specific software requirements. Meanwhile, the lack of systematic engineering methodologies that can effectively encompass precise requirements modeling and rigorous requirements V&V makes the application of formal methods in industry still a big challenge. To tackle this challenge, in this paper, we present a domain experts centric approach to the formal requirements modeling and V&V in the domain of embedded control software. The major advancements of the approach are: 1) a domain-specific and systematic engineering approach to the formal requirements specification construction and 2) scenario-based requirements validation and verification requirements technique. Specifically, the approach offers a domain-specific template for formal specification construction through a three-step specification evolution process. For formal requirements V&V, diagrams are derived from formal specification and domain experts' concerned scenarios can be checked based on the diagrams. These modeling and V&V technologies are coherently incorporated in the approach and fully automated by a supporting tool. We have applied the approach real software projects of our industrial partners. The experimental results show that it significantly facilitates the formal modeling and V&V in industry.
{"title":"A Domain Experts Centric Approach to Formal Requirements Modeling and V&V of Embedded Control Software","authors":"Weikai Miao, Q. Yan, Yihao Huang, Jincao Feng, Hanyue Zheng","doi":"10.1109/APSEC48747.2019.00012","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00012","url":null,"abstract":"Formal method is a promising solution for precise software requirements modeling and V&V (Validation and Verification). However, domain experts are suffering from using complex mathematics formal notations to precisely describe their domain specific software requirements. Meanwhile, the lack of systematic engineering methodologies that can effectively encompass precise requirements modeling and rigorous requirements V&V makes the application of formal methods in industry still a big challenge. To tackle this challenge, in this paper, we present a domain experts centric approach to the formal requirements modeling and V&V in the domain of embedded control software. The major advancements of the approach are: 1) a domain-specific and systematic engineering approach to the formal requirements specification construction and 2) scenario-based requirements validation and verification requirements technique. Specifically, the approach offers a domain-specific template for formal specification construction through a three-step specification evolution process. For formal requirements V&V, diagrams are derived from formal specification and domain experts' concerned scenarios can be checked based on the diagrams. These modeling and V&V technologies are coherently incorporated in the approach and fully automated by a supporting tool. We have applied the approach real software projects of our industrial partners. The experimental results show that it significantly facilitates the formal modeling and V&V in industry.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115577299","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 : 2019-12-01DOI: 10.1109/APSEC48747.2019.00071
Sefa Akça, A. Rajan, Chao Peng
Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools - Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.
{"title":"SolAnalyser: A Framework for Analysing and Testing Smart Contracts","authors":"Sefa Akça, A. Rajan, Chao Peng","doi":"10.1109/APSEC48747.2019.00071","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00071","url":null,"abstract":"Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. To address this challenge, we present a fully automated technique, SolAnalyser, for vulnerability detection over Solidity smart contracts that uses both static and dynamic analysis. Analysis techniques in the literature rely on static analysis with a high rate of false positives or lack support for vulnerabilities like out of gas, unchecked send, timestamp dependency. Our tool, SolAnalyser, supports automated detection of 8 different vulnerability types that currently lack wide support in existing tools, and can easily be extended to support other types. We also implemented a fault seeding tool that injects different types of vulnerabilities in smart contracts. We use the mutated contracts for assessing the effectiveness of different analysis tools. Our experiment uses 1838 real contracts from which we generate 12866 mutated contracts by artificially seeding 8 different vulnerability types. We evaluate the effectiveness of our technique in revealing the seeded vulnerabilities and compare against five existing popular analysis tools - Oyente, Securify, Maian, SmartCheck and Mythril. This is the first large scale evaluation of existing tools that compares their effectiveness by running them on a common set of contracts. We find that our technique outperforms all five existing tools in supporting detection of all 8 vulnerability types and in achieving higher precision and recall rate. SolAnalyser was also faster in analysing the different vulnerabilities than any of the existing tools in our experiment.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824676","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 : 2019-12-01DOI: 10.1109/APSEC48747.2019.00053
Zhangtao Chen, Jing Liu, Xi Ding, Miaomiao Zhang
As a widely used modeling language, AADL (Architecture Analysis and Design Language) plays an important role in designing safety-critical systems. It provides abundant components for describing system architecture and supports the early prediction and repetitive analysis of performance-critical attributes. However, the approach used by AADL to describe the system behavior is based mainly on automata theory; thus, encountering the state space explosion problem when modeling and verifying large and complex systems is inevitable. Furthermore, due to the lack of means to describe the behavior details, it is also difficult for AADL to support the accurate analysis and verification of functional and non-functional requirements. In this paper, we propose a language called RBML that supports refined behavior modeling to compensate for the behavior modeling and verification deficiencies of AADL. This new language is based on AADL but extends the ability to detail various behaviors and allows SMT (Satisfiability Modulo Theories) solvers to verify the constructed refined behavior model, thus alleviating the state space explosion problem to some extent. Experiments on Baidu Apollo are presented to demonstrate the feasibility of our proposed approach.
作为一种广泛使用的建模语言，AADL (Architecture Analysis and Design language)在安全关键型系统的设计中发挥着重要作用。它为描述系统架构提供了丰富的组件，并支持对性能关键属性的早期预测和重复分析。然而，AADL用来描述系统行为的方法主要基于自动机理论;因此，在对大型复杂系统进行建模和验证时，遇到状态空间爆炸问题是不可避免的。此外，由于缺乏描述行为细节的手段，AADL也难以支持对功能和非功能需求的准确分析和验证。本文提出了一种支持精细行为建模的RBML语言，以弥补AADL在行为建模和验证方面的不足。这种新的语言基于AADL，但扩展了详细描述各种行为的能力，并允许SMT (Satisfiability Modulo Theories)求解器验证构造的精细行为模型，从而在一定程度上缓解了状态空间爆炸问题。在百度阿波罗上的实验证明了我们所提出的方法的可行性。
{"title":"RBML: A Refined Behavior Modeling Language for Safety-Critical Hybrid Systems","authors":"Zhangtao Chen, Jing Liu, Xi Ding, Miaomiao Zhang","doi":"10.1109/APSEC48747.2019.00053","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00053","url":null,"abstract":"As a widely used modeling language, AADL (Architecture Analysis and Design Language) plays an important role in designing safety-critical systems. It provides abundant components for describing system architecture and supports the early prediction and repetitive analysis of performance-critical attributes. However, the approach used by AADL to describe the system behavior is based mainly on automata theory; thus, encountering the state space explosion problem when modeling and verifying large and complex systems is inevitable. Furthermore, due to the lack of means to describe the behavior details, it is also difficult for AADL to support the accurate analysis and verification of functional and non-functional requirements. In this paper, we propose a language called RBML that supports refined behavior modeling to compensate for the behavior modeling and verification deficiencies of AADL. This new language is based on AADL but extends the ability to detail various behaviors and allows SMT (Satisfiability Modulo Theories) solvers to verify the constructed refined behavior model, thus alleviating the state space explosion problem to some extent. Experiments on Baidu Apollo are presented to demonstrate the feasibility of our proposed approach.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128969581","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 : 2019-12-01DOI: 10.1109/APSEC48747.2019.00043
Chenghao Cai, Jing Sun, G. Dobbie, S. Lee
This paper proposes a probabilistic reachability repair solution that enables abstract machines to automatically evolve and satisfy desired requirements. The solution is a combination of the B-method, machine learning and program synthesis. The B-method is used to formally specify an abstract machine and analyse the reachability of the abstract machine. Machine learning models are used to approximate features hidden in the semantics of the abstract machine. When the abstract machine fails to reach a desired state, the machine learning models are used to discover missing transitions to the state. Inserting the discovered transitions into the original abstract machine will lead to a repaired abstract machine that is capable of achieving the state. To obtain the repaired abstract machine, a set of insertion repairs are synthesised from the discovered transitions and are simplified using context-free grammars. Experimental results reveal that the reachability repair solution is applicable to a wide range of abstract machines and can accurately discover transitions that satisfy the requirements of reachability. Moreover, the results demonstrate that random forests are efficient machine learning models on transition discovery tasks. Additionally, we argue that the automated reachability repair process can improve the efficiency of software development.
{"title":"Achieving Abstract Machine Reachability with Learning-Based Model Fulfilment","authors":"Chenghao Cai, Jing Sun, G. Dobbie, S. Lee","doi":"10.1109/APSEC48747.2019.00043","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00043","url":null,"abstract":"This paper proposes a probabilistic reachability repair solution that enables abstract machines to automatically evolve and satisfy desired requirements. The solution is a combination of the B-method, machine learning and program synthesis. The B-method is used to formally specify an abstract machine and analyse the reachability of the abstract machine. Machine learning models are used to approximate features hidden in the semantics of the abstract machine. When the abstract machine fails to reach a desired state, the machine learning models are used to discover missing transitions to the state. Inserting the discovered transitions into the original abstract machine will lead to a repaired abstract machine that is capable of achieving the state. To obtain the repaired abstract machine, a set of insertion repairs are synthesised from the discovered transitions and are simplified using context-free grammars. Experimental results reveal that the reachability repair solution is applicable to a wide range of abstract machines and can accurately discover transitions that satisfy the requirements of reachability. Moreover, the results demonstrate that random forests are efficient machine learning models on transition discovery tasks. Additionally, we argue that the automated reachability repair process can improve the efficiency of software development.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127643592","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 : 2019-12-01DOI: 10.1109/APSEC48747.2019.00051
Xueying Li, Peng Liang, Tianqing Liu
Stakeholders make various decisions regarding requirements, architectural design, project management, etc. during the life cycle of software development. They have an interest in making high-quality and appropriate decisions that meet project objectives and maximize system benefits. However, decision-making in software development is not a trivial task and the decisions made are often not well documented. In this paper, we analyzed 9006 posts from the Hibernate developer mailing list, in order to explore (1) decision expression and (2) classification, (3) the rationale behind decision-making, (4) approaches employed in decision-making, (5) related software artifacts of decision-making, and (6) the trend of decision-making over time. The results show that (1) all decisions are expressed as Information Giving, Solution Proposal, and Feature Request; (2) the main categories of decisions are Design Decision and Requirement Decision; (3) the most common rationale behind decision-making is Non-functional Requirement; (4) the most commonly used decision-making approach is Problem Structuring; (5) the main categories of software artifacts related to decision-making are Design Document and Requirement Document; and (6) decision-making runs through the whole software development life cycle.
{"title":"Decisions and Their Making in OSS Development: An Exploratory Study Using the Hibernate Developer Mailing List","authors":"Xueying Li, Peng Liang, Tianqing Liu","doi":"10.1109/APSEC48747.2019.00051","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00051","url":null,"abstract":"Stakeholders make various decisions regarding requirements, architectural design, project management, etc. during the life cycle of software development. They have an interest in making high-quality and appropriate decisions that meet project objectives and maximize system benefits. However, decision-making in software development is not a trivial task and the decisions made are often not well documented. In this paper, we analyzed 9006 posts from the Hibernate developer mailing list, in order to explore (1) decision expression and (2) classification, (3) the rationale behind decision-making, (4) approaches employed in decision-making, (5) related software artifacts of decision-making, and (6) the trend of decision-making over time. The results show that (1) all decisions are expressed as Information Giving, Solution Proposal, and Feature Request; (2) the main categories of decisions are Design Decision and Requirement Decision; (3) the most common rationale behind decision-making is Non-functional Requirement; (4) the most commonly used decision-making approach is Problem Structuring; (5) the main categories of software artifacts related to decision-making are Design Document and Requirement Document; and (6) decision-making runs through the whole software development life cycle.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117218099","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 : 2019-12-01DOI: 10.1109/APSEC48747.2019.00069
Chao Peng, Sefa Akça, A. Rajan
Solidity is an object-oriented and high-level language for writing smart contracts that are used to execute, verify and enforce credible transactions on permissionless blockchains. In the last few years, analysis of smart contracts has raised considerable interest and numerous techniques have been proposed to check the presence of vulnerabilities in them. Current techniques lack traceability in source code and have widely differing work flows. There is no single unifying framework for analysis, instrumentation, optimisation and code generation of Solidity contracts at the source code level. In this paper, we present SIF, a comprehensive framework for Solidity contract analysis, query, instrumentation, and code generation. SIF provides support for Solidity contract developers and testers to build source level techniques for analysis, understanding, diagnostics, optimisations and code generation. We show feasibility and applicability of the framework by building practical tools on top of it and running them on 1838 real smart contracts deployed on the Ethereum network.
{"title":"SIF: A Framework for Solidity Contract Instrumentation and Analysis","authors":"Chao Peng, Sefa Akça, A. Rajan","doi":"10.1109/APSEC48747.2019.00069","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00069","url":null,"abstract":"Solidity is an object-oriented and high-level language for writing smart contracts that are used to execute, verify and enforce credible transactions on permissionless blockchains. In the last few years, analysis of smart contracts has raised considerable interest and numerous techniques have been proposed to check the presence of vulnerabilities in them. Current techniques lack traceability in source code and have widely differing work flows. There is no single unifying framework for analysis, instrumentation, optimisation and code generation of Solidity contracts at the source code level. In this paper, we present SIF, a comprehensive framework for Solidity contract analysis, query, instrumentation, and code generation. SIF provides support for Solidity contract developers and testers to build source level techniques for analysis, understanding, diagnostics, optimisations and code generation. We show feasibility and applicability of the framework by building practical tools on top of it and running them on 1838 real smart contracts deployed on the Ethereum network.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127300417","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 : 2019-12-01DOI: 10.1109/APSEC48747.2019.00049
S. Tiwari, S. Rathore
A software process model (SPM) provides an abstract description of the order in which related activities of software development will be undertaken. Many process models available that can be adapted for software development. However, the selection of the best suitable process model with reference to the problem definition, constraints, and stakeholder requirements is a challenging task. Typically, in a Software Engineering (SE) course, students gain knowledge about SPM and realize their usage via classroom lectures and course projects. It is felt that if the basic knowledge imparted, through the fundamental SE course, is supplemented with some focused sessions about the SPM, then it will not only enable students to think in terms of the SPM but will also motivate them to harness the best practices of software development. This paper presents a preliminary study highlighting our experience on SPM-oriented teaching to impart the concept of requirement elicitation and process modeling, by performing a play (drama skit) annotating real-world scenarios. The feedbacks of students have been collected to evaluate whether this exercise helped them in understanding the processes they have to undergo during software development. Additionally, we have compared the student's feedback and performance in the project and reported the finding of the study.
{"title":"Teaching Software Process Models to Software Engineering Students: An Exploratory Study","authors":"S. Tiwari, S. Rathore","doi":"10.1109/APSEC48747.2019.00049","DOIUrl":"https://doi.org/10.1109/APSEC48747.2019.00049","url":null,"abstract":"A software process model (SPM) provides an abstract description of the order in which related activities of software development will be undertaken. Many process models available that can be adapted for software development. However, the selection of the best suitable process model with reference to the problem definition, constraints, and stakeholder requirements is a challenging task. Typically, in a Software Engineering (SE) course, students gain knowledge about SPM and realize their usage via classroom lectures and course projects. It is felt that if the basic knowledge imparted, through the fundamental SE course, is supplemented with some focused sessions about the SPM, then it will not only enable students to think in terms of the SPM but will also motivate them to harness the best practices of software development. This paper presents a preliminary study highlighting our experience on SPM-oriented teaching to impart the concept of requirement elicitation and process modeling, by performing a play (drama skit) annotating real-world scenarios. The feedbacks of students have been collected to evaluate whether this exercise helped them in understanding the processes they have to undergo during software development. Additionally, we have compared the student's feedback and performance in the project and reported the finding of the study.","PeriodicalId":325642,"journal":{"name":"2019 26th Asia-Pacific Software Engineering Conference (APSEC)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121435692","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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