Pub Date : 2021-10-07DOI: 10.1109/APSEC53868.2021.00040
Kien-Tuan Ngo, Dinh-Truong Do, Thu-Trang Nguyen, H. Vo
Static analysis tools are frequently used to detect potential vulnerabilities in software systems. However, an inevitable problem of these tools is their large number of warnings with a high false positive rate, which consumes time and effort for investigating. In this paper, we present DEFP, a novel method for ranking static analysis warnings. Based on the intuition that warnings which have similar contexts tend to have similar labels (true positive or false positive), DEFP is built with two BiLSTM models to capture the patterns associated with the contexts of labeled warnings. After that, for a set of new warnings, DEFP can calculate and rank them according to their likelihoods to be true positives (i.e., actual vulnerabilities). Our experimental results on a dataset of 10 real-world projects show that using DEFP, by investigating only 60% of the warnings, developers can find +90% of actual vulnerabilities. Moreover, DEFP improves the state-of-the-art approach 30% in both Precision and Recall.
{"title":"Ranking Warnings of Static Analysis Tools Using Representation Learning","authors":"Kien-Tuan Ngo, Dinh-Truong Do, Thu-Trang Nguyen, H. Vo","doi":"10.1109/APSEC53868.2021.00040","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00040","url":null,"abstract":"Static analysis tools are frequently used to detect potential vulnerabilities in software systems. However, an inevitable problem of these tools is their large number of warnings with a high false positive rate, which consumes time and effort for investigating. In this paper, we present DEFP, a novel method for ranking static analysis warnings. Based on the intuition that warnings which have similar contexts tend to have similar labels (true positive or false positive), DEFP is built with two BiLSTM models to capture the patterns associated with the contexts of labeled warnings. After that, for a set of new warnings, DEFP can calculate and rank them according to their likelihoods to be true positives (i.e., actual vulnerabilities). Our experimental results on a dataset of 10 real-world projects show that using DEFP, by investigating only 60% of the warnings, developers can find +90% of actual vulnerabilities. Moreover, DEFP improves the state-of-the-art approach 30% in both Precision and Recall.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115993304","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 : 2021-10-05DOI: 10.1109/APSEC53868.2021.00063
Humphrey O. Obie, Mojtaba Shahin, John C. Grundy, Burak Turhan, Li Li, Waqar Hussain, J. Whittle
The elicitation of end-users& human values - such as freedom, honesty, transparency, etc - is important in the development of software systems. We carried out two preliminary Q-studies to understand (a) the general human value opinion types of eHealth applications (apps) end-users (b) the eHealth domain human value opinion types of eHealth apps end-users (c) whether there are differences between the general and eHealth domain opinion types. Our early results show three value opinion types using generic value instruments: (1) fun-loving, success-driven and independent end-user, (2) security-conscious, socially-concerned, and success-driven end-user, and (3) benevolent, success-driven, and conformist end-user. Our results also show two value opinion types using domain-specific value instruments: (1) security-conscious, reputable, and honest end-user, and (2) success-driven, reputable and pain-avoiding end-user. Given these results, consideration should be given to domain context in the design and application of values elicitation instruments.
{"title":"Does Domain Change the Opinion of Individuals on Human Values? A Preliminary Investigation on eHealth Apps End-users","authors":"Humphrey O. Obie, Mojtaba Shahin, John C. Grundy, Burak Turhan, Li Li, Waqar Hussain, J. Whittle","doi":"10.1109/APSEC53868.2021.00063","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00063","url":null,"abstract":"The elicitation of end-users& human values - such as freedom, honesty, transparency, etc - is important in the development of software systems. We carried out two preliminary Q-studies to understand (a) the general human value opinion types of eHealth applications (apps) end-users (b) the eHealth domain human value opinion types of eHealth apps end-users (c) whether there are differences between the general and eHealth domain opinion types. Our early results show three value opinion types using generic value instruments: (1) fun-loving, success-driven and independent end-user, (2) security-conscious, socially-concerned, and success-driven end-user, and (3) benevolent, success-driven, and conformist end-user. Our results also show two value opinion types using domain-specific value instruments: (1) security-conscious, reputable, and honest end-user, and (2) success-driven, reputable and pain-avoiding end-user. Given these results, consideration should be given to domain context in the design and application of values elicitation instruments.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124671758","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 : 2021-09-26DOI: 10.1109/APSEC53868.2021.00031
Yuchu Liu, D. I. Mattos, J. Bosch, H. H. Olsson, Jonn Lantz
Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.
{"title":"Bayesian propensity score matching in automotive embedded software engineering","authors":"Yuchu Liu, D. I. Mattos, J. Bosch, H. H. Olsson, Jonn Lantz","doi":"10.1109/APSEC53868.2021.00031","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00031","url":null,"abstract":"Randomised field experiments, such as A/B testing, have long been the gold standard for evaluating the value that new software brings to customers. However, running randomised field experiments is not always desired, possible or even ethical in the development of automotive embedded software. In the face of such restrictions, we propose the use of the Bayesian propensity score matching technique for causal inference of observational studies in the automotive domain. In this paper, we present a method based on the Bayesian propensity score matching framework, applied in the unique setting of automotive software engineering. This method is used to generate balanced control and treatment groups from an observational online evaluation and estimate causal treatment effects from the software changes, even with limited samples in the treatment group. We exemplify the method with a proof-of-concept in the automotive domain. In the example, we have a larger control (Nc = 1100) fleet of cars using the current software and a small treatment fleet (Nt = 38), in which we introduce a new software variant. We demonstrate a scenario that shipping of a new software to all users is restricted, as a result, a fully randomised experiment could not be conducted. Therefore, we utilised the Bayesian propensity score matching method with 14 observed covariates as inputs. The results show more balanced groups, suitable for estimating causal treatment effects from the collected observational data. We describe the method in detail and share our configuration. Furthermore, we discuss how can such a method be used for online evaluation of new software utilising small groups of samples.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125204714","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 : 2021-09-20DOI: 10.1109/APSEC53868.2021.00036
Benjamin Koh, Mojtaba Shahin, Annette Ong, Soo Ying Yeap, Priyanka Saxena, M. Singh, Chunyang Chen
The COVID-19 pandemic has birthed a wealth of information through many publicly accessible sources, such as news outlets and social media. However, gathering and understanding the content can be difficult due to inaccuracies or inconsistencies between the different sources. To alleviate this challenge in Australia, a team of 48 student volunteers developed an open-source COVID-19 information dashboard to provide accurate, reliable, and real-time COVID-19 information for Australians. The students developed this software while working under legislative restrictions that required social isolation. The goal of this study is to characterize the experiences of the students throughout the project. We conducted an online survey completed by 39 of the volunteering students contributing to the COVID-19 dashboard project. Our results indicate that playing a positive role in the COVID-19 crisis and learning new skills and technologies were the most cited motivating factors for the students to participate in the project. While working on the project, some students struggled to maintain a work-life balance due to working from home. However, the students generally did not express strong sentiment towards general project challenges. The students expressed more strongly that data collection was a significant challenge as it was difficult to collect reliable, accurate, and up-to-date data from various government sources. The students have been able to mitigate these challenges by establishing a systematic data collection process in the team, leveraging frequent and clear communication through text, and appreciating and encouraging each other's efforts. By participating in the project, the students boosted their technical (e.g., front-end development) and nontechnical (e.g., task prioritization) skills. Our study discusses several implications for students, educators, and policymakers.
{"title":"Pandemic Software Development: The Student Experiences from Developing a COVID-19 Information Dashboard","authors":"Benjamin Koh, Mojtaba Shahin, Annette Ong, Soo Ying Yeap, Priyanka Saxena, M. Singh, Chunyang Chen","doi":"10.1109/APSEC53868.2021.00036","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00036","url":null,"abstract":"The COVID-19 pandemic has birthed a wealth of information through many publicly accessible sources, such as news outlets and social media. However, gathering and understanding the content can be difficult due to inaccuracies or inconsistencies between the different sources. To alleviate this challenge in Australia, a team of 48 student volunteers developed an open-source COVID-19 information dashboard to provide accurate, reliable, and real-time COVID-19 information for Australians. The students developed this software while working under legislative restrictions that required social isolation. The goal of this study is to characterize the experiences of the students throughout the project. We conducted an online survey completed by 39 of the volunteering students contributing to the COVID-19 dashboard project. Our results indicate that playing a positive role in the COVID-19 crisis and learning new skills and technologies were the most cited motivating factors for the students to participate in the project. While working on the project, some students struggled to maintain a work-life balance due to working from home. However, the students generally did not express strong sentiment towards general project challenges. The students expressed more strongly that data collection was a significant challenge as it was difficult to collect reliable, accurate, and up-to-date data from various government sources. The students have been able to mitigate these challenges by establishing a systematic data collection process in the team, leveraging frequent and clear communication through text, and appreciating and encouraging each other's efforts. By participating in the project, the students boosted their technical (e.g., front-end development) and nontechnical (e.g., task prioritization) skills. Our study discusses several implications for students, educators, and policymakers.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115274306","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 : 2021-04-26DOI: 10.1109/APSEC53868.2021.00037
Yong-Jun Shin, Joon-Young Bae, Doo-Hwan Bae
The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issues from runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of environments in SAS development remains ambiguous and abstract. To make this knowledge more concrete, we investigated concepts and models of the environment covered in this area through a systematic literature review (SLR). We automatically and manually searched 3719 papers and selected 128 papers as primary studies. We explored and analyzed concepts of the environment covered in the primary studies and investigated cases in which the concepts were specifically expressed as environment models. In doing so, we provide trends of how SAS academia understands the environment of SAS. Specifically, this SLR provides five common characteristics of the environment, two common sources of the environmental uncertainty, and 14 reference environment models with various purpose and expressiveness. Finally, we summarized lessons learned through this SLR and directions for future SAS research on the basis of the concrete knowledge of the SAS environment.
{"title":"Concepts and Models of Environment of Self-Adaptive Systems: A Systematic Literature Review","authors":"Yong-Jun Shin, Joon-Young Bae, Doo-Hwan Bae","doi":"10.1109/APSEC53868.2021.00037","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00037","url":null,"abstract":"The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issues from runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of environments in SAS development remains ambiguous and abstract. To make this knowledge more concrete, we investigated concepts and models of the environment covered in this area through a systematic literature review (SLR). We automatically and manually searched 3719 papers and selected 128 papers as primary studies. We explored and analyzed concepts of the environment covered in the primary studies and investigated cases in which the concepts were specifically expressed as environment models. In doing so, we provide trends of how SAS academia understands the environment of SAS. Specifically, this SLR provides five common characteristics of the environment, two common sources of the environmental uncertainty, and 14 reference environment models with various purpose and expressiveness. Finally, we summarized lessons learned through this SLR and directions for future SAS research on the basis of the concrete knowledge of the SAS environment.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133171599","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 : 2021-02-12DOI: 10.1109/APSEC53868.2021.00029
Guang Yang, Yanlin Zhou, Xiang Chen, Chi Yu
Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo.
{"title":"Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer","authors":"Guang Yang, Yanlin Zhou, Xiang Chen, Chi Yu","doi":"10.1109/APSEC53868.2021.00029","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00029","url":null,"abstract":"Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128174400","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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