Pub Date : 2022-08-01DOI: 10.1109/seaa56994.2022.00005
{"title":"Message from the SEAA 2022 General Chair","authors":"","doi":"10.1109/seaa56994.2022.00005","DOIUrl":"https://doi.org/10.1109/seaa56994.2022.00005","url":null,"abstract":"","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123669512","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00080
Fábio Octaviano, K. Felizardo, S. Fabbri, B. Napoleão, Fábio Petrillo, Sylvain Hallé
Context: There are several initiatives to semi-automate the initial selection of studies task for Systematic Literature Reviews (SLR) to reduce effort and potential bias. Objective: We propose a strategy called SCAS-AI to semi-automate the initial selection task. This strategy improves the original SCAS strategy with Artificial Intelligence (AI) resources (fuzzy logic and genetic algorithm) for studies selection. Method: We evaluated the SCAS-AI strategy through a quasi-experiment with SLRs in Software Engineering (SE). Results: In general, the SCAS-AI strategy improved the results achieved using the original SCAS strategy in reducing the effort of the initial selection task. The effort reduction applying SCAS-AI was 39.1%. In addition, the errors percentage was 0.3% for studies automatically excluded (false negative – loss of evidence) and 3.3% for studies automatically included (false positive – evidence later excluded during the full-text reading). Conclusion: The results show the potential of the investigated AI techniques to support the initial selection task for SLRs in SE.
{"title":"SCAS-AI: A Strategy to Semi-Automate the Initial Selection Task in Systematic Literature Reviews","authors":"Fábio Octaviano, K. Felizardo, S. Fabbri, B. Napoleão, Fábio Petrillo, Sylvain Hallé","doi":"10.1109/SEAA56994.2022.00080","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00080","url":null,"abstract":"Context: There are several initiatives to semi-automate the initial selection of studies task for Systematic Literature Reviews (SLR) to reduce effort and potential bias. Objective: We propose a strategy called SCAS-AI to semi-automate the initial selection task. This strategy improves the original SCAS strategy with Artificial Intelligence (AI) resources (fuzzy logic and genetic algorithm) for studies selection. Method: We evaluated the SCAS-AI strategy through a quasi-experiment with SLRs in Software Engineering (SE). Results: In general, the SCAS-AI strategy improved the results achieved using the original SCAS strategy in reducing the effort of the initial selection task. The effort reduction applying SCAS-AI was 39.1%. In addition, the errors percentage was 0.3% for studies automatically excluded (false negative – loss of evidence) and 3.3% for studies automatically included (false positive – evidence later excluded during the full-text reading). Conclusion: The results show the potential of the investigated AI techniques to support the initial selection task for SLRs in SE.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127324612","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00043
Gabriella Andrade, Dalvan Griebler, R. Santos, C. Kessler, August Ernstsson, L. G. Fernandes
Over the years, several Parallel Programming Models (PPMs) have supported the abstraction of programming complexity for parallel computer systems. However, few studies aim to evaluate the productivity reached by such abstractions since this is a complex task that involves human beings. There are several studies to develop predictive methods to estimate the effort required to develop software applications. In order to evaluate the reliability of such metrics, it is necessary to assess the accuracy in different programming paradigms. In this work, we used the data of an experiment conducted with beginners in parallel programming to determine the effort required for implementing stream parallelism using FastFlow, SPar, and TBB. Our results show that some traditional software effort estimation models, such as COCOMO II, fall short. In contrast, Planning Poker could contribute toward a parallel-aware effort model.
{"title":"Analyzing Programming Effort Model Accuracy of High-Level Parallel Programs for Stream Processing","authors":"Gabriella Andrade, Dalvan Griebler, R. Santos, C. Kessler, August Ernstsson, L. G. Fernandes","doi":"10.1109/SEAA56994.2022.00043","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00043","url":null,"abstract":"Over the years, several Parallel Programming Models (PPMs) have supported the abstraction of programming complexity for parallel computer systems. However, few studies aim to evaluate the productivity reached by such abstractions since this is a complex task that involves human beings. There are several studies to develop predictive methods to estimate the effort required to develop software applications. In order to evaluate the reliability of such metrics, it is necessary to assess the accuracy in different programming paradigms. In this work, we used the data of an experiment conducted with beginners in parallel programming to determine the effort required for implementing stream parallelism using FastFlow, SPar, and TBB. Our results show that some traditional software effort estimation models, such as COCOMO II, fall short. In contrast, Planning Poker could contribute toward a parallel-aware effort model.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122903298","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00065
Misael Alpizar Santana, R. Calinescu, Colin Paterson
Deep Neural Network (DNN) classifiers perform remarkably well on many problems that require skills which are natural and intuitive to humans. These classifiers have been used in safety-critical systems including autonomous vehicles. For such systems to be trusted it is necessary to demonstrate that the risk factors associated with neural network classification have been appropriately considered and sufficient risk mitigation has been employed. Traditional DNNs fail to explicitly consider risk during their training and verification stages, meaning that unsafe failure modes are permitted and under-reported. To address this limitation, our short paper introduces a work-in-progress approach that (i) allows the risk of misclassification between classes to be quantified, (ii) guides the training of DNN classifiers towards mitigating the risks that require treatment, and (iii) synthesises risk-aware ensembles with the aid of multi-objective genetic algorithms that seek to optimise DNN performance metrics while also mitigating risks. We show the effectiveness of our approach by using it to synthesise risk-aware neural network ensembles for the CIFAR-10 dataset.
{"title":"Mitigating Risk in Neural Network Classifiers","authors":"Misael Alpizar Santana, R. Calinescu, Colin Paterson","doi":"10.1109/SEAA56994.2022.00065","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00065","url":null,"abstract":"Deep Neural Network (DNN) classifiers perform remarkably well on many problems that require skills which are natural and intuitive to humans. These classifiers have been used in safety-critical systems including autonomous vehicles. For such systems to be trusted it is necessary to demonstrate that the risk factors associated with neural network classification have been appropriately considered and sufficient risk mitigation has been employed. Traditional DNNs fail to explicitly consider risk during their training and verification stages, meaning that unsafe failure modes are permitted and under-reported. To address this limitation, our short paper introduces a work-in-progress approach that (i) allows the risk of misclassification between classes to be quantified, (ii) guides the training of DNN classifiers towards mitigating the risks that require treatment, and (iii) synthesises risk-aware ensembles with the aid of multi-objective genetic algorithms that seek to optimise DNN performance metrics while also mitigating risks. We show the effectiveness of our approach by using it to synthesise risk-aware neural network ensembles for the CIFAR-10 dataset.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134274982","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00037
Simone Romano, G. Scanniello, Pancrazio Dionisio
The Software Engineering (SE) research community has been showing an increasing interest in peopleware, which refers to anything that has to do with the role of human factors in software development. Individuals’ personality is one of the human factors that can affect software development. In this paper, we present the results of a preliminary empirical study to understand whether there is a relationship between the personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, and neuroticism) and productivity of undergraduate students in Computer Science (CS), and internal quality of the programs they developed in an implementation task. In our study, we involved 30 (last-year) undergraduate students in CS, who had to implement a series of features. Our results suggest that there are correlation relationships between some personality traits (i.e., conscientiousness, extraversion, and neuroticism) and software quality. As for productivity, we could not find any correlation relationship.
{"title":"On the Role of Personality Traits in Implementation Tasks: A Preliminary Investigation with Students","authors":"Simone Romano, G. Scanniello, Pancrazio Dionisio","doi":"10.1109/SEAA56994.2022.00037","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00037","url":null,"abstract":"The Software Engineering (SE) research community has been showing an increasing interest in peopleware, which refers to anything that has to do with the role of human factors in software development. Individuals’ personality is one of the human factors that can affect software development. In this paper, we present the results of a preliminary empirical study to understand whether there is a relationship between the personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, and neuroticism) and productivity of undergraduate students in Computer Science (CS), and internal quality of the programs they developed in an implementation task. In our study, we involved 30 (last-year) undergraduate students in CS, who had to implement a series of features. Our results suggest that there are correlation relationships between some personality traits (i.e., conscientiousness, extraversion, and neuroticism) and software quality. As for productivity, we could not find any correlation relationship.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115068329","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00062
Rodi Jolak, Thomas Rosenstatter, Saif Aldaghistani, R. Scandariato
The automotive domain has got its own share of advancements in information and communication technology, providing more services and leading to more connectivity. However, more connectivity and openness raise cyber security and safety concerns. Indeed, services that depend on online connectivity can serve as entry points for attacks on different assets of the vehicle. This study explores collaborative ways of selecting response techniques to counter real-time cyber attacks on automotive systems. The aim is to mitigate the attacks more quickly than a single vehicle would be able to do, and increase the survivability chances of the collaborating vehicles. To achieve that, the design science research methodology is employed. As a result, we present RIPOSTE, a framework for collaborative real-time evaluation and selection of suitable response techniques when an attack is in progress. We evaluate the framework from a safety perspective by conducting a qualitative study involving domain experts. The proposed framework is deemed slightly unsafe, and insights into how to improve the overall safety of the framework are provided.
{"title":"RIPOSTE: A Collaborative Cyber Attack Response Framework for Automotive Systems","authors":"Rodi Jolak, Thomas Rosenstatter, Saif Aldaghistani, R. Scandariato","doi":"10.1109/SEAA56994.2022.00062","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00062","url":null,"abstract":"The automotive domain has got its own share of advancements in information and communication technology, providing more services and leading to more connectivity. However, more connectivity and openness raise cyber security and safety concerns. Indeed, services that depend on online connectivity can serve as entry points for attacks on different assets of the vehicle. This study explores collaborative ways of selecting response techniques to counter real-time cyber attacks on automotive systems. The aim is to mitigate the attacks more quickly than a single vehicle would be able to do, and increase the survivability chances of the collaborating vehicles. To achieve that, the design science research methodology is employed. As a result, we present RIPOSTE, a framework for collaborative real-time evaluation and selection of suitable response techniques when an attack is in progress. We evaluate the framework from a safety perspective by conducting a qualitative study involving domain experts. The proposed framework is deemed slightly unsafe, and insights into how to improve the overall safety of the framework are provided.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127635531","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00076
Leif Bonorden
Application Programming Interfaces (APIs) are the prevalent interaction method for software modules, components, and systems. As systems and APIs evolve, an API element may be marked as deprecated, indicating that its use is disapproved or that the feature will be removed in an upcoming version. Consequently, deprecation is a means of communication between developers and, ideally, complemented by further documentation, including suggestions for the developers of the API’s clients.API deprecation is a relatively young research area that recently gained traction among researchers. To identify the current state of research as well as to identify open research areas, a meta-study that assesses scientific studies is necessary. Therefore, this paper presents a systematic mapping study on API deprecation to classify the state of the art and identify gaps in the research field. We identified and mapped 36 primary studies into a classification scheme comprising general and API-specific categories.We identified five major gaps in previous research on API deprecation as opportunities for future studies: studying remote APIs, investigating a broader range of static APIs, joining suppliers’ and clients’ views, including humans in studies, and avoiding deprecation.
{"title":"API Deprecation: A Systematic Mapping Study","authors":"Leif Bonorden","doi":"10.1109/SEAA56994.2022.00076","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00076","url":null,"abstract":"Application Programming Interfaces (APIs) are the prevalent interaction method for software modules, components, and systems. As systems and APIs evolve, an API element may be marked as deprecated, indicating that its use is disapproved or that the feature will be removed in an upcoming version. Consequently, deprecation is a means of communication between developers and, ideally, complemented by further documentation, including suggestions for the developers of the API’s clients.API deprecation is a relatively young research area that recently gained traction among researchers. To identify the current state of research as well as to identify open research areas, a meta-study that assesses scientific studies is necessary. Therefore, this paper presents a systematic mapping study on API deprecation to classify the state of the art and identify gaps in the research field. We identified and mapped 36 primary studies into a classification scheme comprising general and API-specific categories.We identified five major gaps in previous research on API deprecation as opportunities for future studies: studying remote APIs, investigating a broader range of static APIs, joining suppliers’ and clients’ views, including humans in studies, and avoiding deprecation.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122051309","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00069
Anne Bumiller, Olivier Barais, Stéphanie Challita, B. Combemale, Nicolas Aillery, Gaël Le Lan
Nowadays, many mechanisms exist to perform authentication, such as text passwords and biometrics. However, reasoning about their relevance (e.g., the appropriateness for security and usability) regarding the contextual situation is challenging for authentication system designers. In this paper, we present a Context-driven Modelling Framework for dynamic Authentication decisions (COFRA), where the context information specifies the relevance of authentication mechanisms. COFRA is based on a precise metamodel that reveals framework abstractions and a set of constraints that specify their meaning. Therefore, it provides a language to determine the relevant authentication mechanisms (characterized by properties that ensure their appropriateness) in a given context. The framework supports the adaptive authentication system designers in the complex trade-off analysis between context information, risks and authentication mechanisms, according to usability, deployability, security, and privacy. We validate the proposed framework through case studies and extensive exchanges with authentication and modelling experts. We show that model instances describing real-world use cases and authentication approaches proposed in the literature can be instantiated validly according to our metamodel. This validation highlights the necessity, sufficiency, and soundness of our framework.
{"title":"A Context-Driven Modelling Framework for Dynamic Authentication Decisions","authors":"Anne Bumiller, Olivier Barais, Stéphanie Challita, B. Combemale, Nicolas Aillery, Gaël Le Lan","doi":"10.1109/SEAA56994.2022.00069","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00069","url":null,"abstract":"Nowadays, many mechanisms exist to perform authentication, such as text passwords and biometrics. However, reasoning about their relevance (e.g., the appropriateness for security and usability) regarding the contextual situation is challenging for authentication system designers. In this paper, we present a Context-driven Modelling Framework for dynamic Authentication decisions (COFRA), where the context information specifies the relevance of authentication mechanisms. COFRA is based on a precise metamodel that reveals framework abstractions and a set of constraints that specify their meaning. Therefore, it provides a language to determine the relevant authentication mechanisms (characterized by properties that ensure their appropriateness) in a given context. The framework supports the adaptive authentication system designers in the complex trade-off analysis between context information, risks and authentication mechanisms, according to usability, deployability, security, and privacy. We validate the proposed framework through case studies and extensive exchanges with authentication and modelling experts. We show that model instances describing real-world use cases and authentication approaches proposed in the literature can be instantiated validly according to our metamodel. This validation highlights the necessity, sufficiency, and soundness of our framework.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122233067","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 : 2022-08-01DOI: 10.1109/SEAA56994.2022.00019
Hongyi Zhang, Jingya Li, Z. Qi, Xingqin Lin, Anders Aronsson, Jan Bosch, H. H. Olsson
Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.
{"title":"Deep Reinforcement Learning in a Dynamic Environment: A Case Study in the Telecommunication Industry","authors":"Hongyi Zhang, Jingya Li, Z. Qi, Xingqin Lin, Anders Aronsson, Jan Bosch, H. H. Olsson","doi":"10.1109/SEAA56994.2022.00019","DOIUrl":"https://doi.org/10.1109/SEAA56994.2022.00019","url":null,"abstract":"Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.","PeriodicalId":269970,"journal":{"name":"2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130682219","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：