Search Results - software engineering/programming and operating system*

Testing machine learning based systems: a systematic mapping by Riccio, Vincenzo, Jahangirova, Gunel, Stocco, Andrea, Humbatova, Nargiz, Weiss, Michael, Tonella, Paolo

“…Context: A Machine Learning based System (MLS) is a software system including one or more components that learn how to perform a task from a given data set…”
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An empirical comparison of dependency network evolution in seven software packaging ecosystems by Decan, Alexandre, Mens, Tom, Grosjean, Philippe

“… The software packages distributed by such package managers form large software ecosystems. These packaging ecosystems contain a large number of package releases that are updated regularly and that have many dependencies to other package releases…”
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AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems by Sarker, Iqbal H.

“…), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key…”
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Test case selection and prioritization using machine learning: a systematic literature review by Pan, Rongqi, Bagherzadeh, Mojtaba, Ghaleb, Taher A., Briand, Lionel

“…Regression testing is an essential activity to assure that software code changes do not adversely affect existing functionalities…”
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MCMAS: an open-source model checker for the verification of multi-agent systems by Lomuscio, Alessio, Qu, Hongyang, Raimondi, Franco

“…We present MCMAS, a model checker for the verification of multi-agent systems. MCMAS supports efficient symbolic techniques for the verification of multi-agent…”
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Data cleaning and machine learning: a systematic literature review by Côté, Pierre-Olivier, Nikanjam, Amin, Ahmed, Nafisa, Humeniuk, Dmytro, Khomh, Foutse

“…Machine Learning (ML) is integrated into a growing number of systems for various applications…”
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Explainability in human–agent systems by Rosenfeld, Avi, Richardson, Ariella

“…This paper presents a taxonomy of explainability in human–agent systems. We consider fundamental questions about the Why, Who, What, When…”
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How to certify machine learning based safety-critical systems? A systematic literature review by Tambon, Florian, Laberge, Gabriel, An, Le, Nikanjam, Amin, Mindom, Paulina Stevia Nouwou, Pequignot, Yann, Khomh, Foutse, Antoniol, Giulio, Merlo, Ettore, Laviolette, François

“… “How to Certify Machine Learning Based Safety-critical Systems?”. Method We conduct a Systematic Literature Review (SLR…”
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Sampling in software engineering research: a critical review and guidelines by Baltes, Sebastian, Ralph, Paul

“…Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field…”
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Perceived diversity in software engineering: a systematic literature review by Rodríguez-Pérez, Gema, Nadri, Reza, Nagappan, Meiyappan

“… with. Perceived diversity in Software Engineering has been recognized as a high-value team property and companies are willing to increase their efforts to create more diverse work teams…”
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A comprehensive study of bloated dependencies in the Maven ecosystem by Soto-Valero, César, Harrand, Nicolas, Monperrus, Martin, Baudry, Benoit

“…Build automation tools and package managers have a profound influence on software development…”
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Development of Smart Healthcare Monitoring System in IoT Environment by Islam, Md. Milon, Rahaman, Ashikur, Islam, Md. Rashedul

“…Healthcare monitoring system in hospitals and many other health centers has experienced significant growth, and portable healthcare monitoring systems with emerging technologies are becoming of great…”
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Logic-based technologies for multi-agent systems: a systematic literature review by Calegari, Roberta, Ciatto, Giovanni, Mascardi, Viviana, Omicini, Andrea

“…”—in particular, logic-based ones—will take place in the next few years. On the other hand, agents and multi-agent systems (MAS…”
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An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems by Selvarajan, Shitharth, Srivastava, Gautam, Khadidos, Alaa O., Khadidos, Adil O., Baza, Mohamed, Alshehri, Ali, Lin, Jerry Chun-Wei

“…, intelligent data, predictive analytics, and decision-making systems for improved market performance. However, traditional IIoT architectures are highly susceptible to many…”
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Can Offline Testing of Deep Neural Networks Replace Their Online Testing?: A Case Study of Automated Driving Systems by Haq, Fitash Ul, Shin, Donghwan, Nejati, Shiva, Briand, Lionel

“…We distinguish two general modes of testing for Deep Neural Networks (DNNs): Offline testing where DNNs are tested as individual units based on test datasets…”
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Intrusion detection systems for IoT-based smart environments: a survey by Elrawy, Mohamed Faisal, Awad, Ali Ismail, Hamed, Hesham F. A.

“… The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications…”
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Thirteen years of SysML: a systematic mapping study by Wolny, Sabine, Mazak, Alexandra, Carpella, Christine, Geist, Verena, Wimmer, Manuel

“… This standard is an extended subset of UML providing a graphical modeling language for designing complex systems by considering software as well as hardware parts…”
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Understanding the challenges and novel architectural models of multi-cloud native applications – a systematic literature review by Alonso, Juncal, Orue-Echevarria, Leire, Casola, Valentina, Torre, Ana Isabel, Huarte, Maider, Osaba, Eneko, Lobo, Jesus L.

“…, deployed and operated over such heterogeneous ecosystems. The result is an effective…”
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Industry practices and challenges for the evolvability assurance of microservices: An interview study and systematic grey literature review by Bogner, Justus, Fritzsch, Jonas, Wagner, Stefan, Zimmermann, Alfred

“…Context Microservices as a lightweight and decentralized architectural style with fine-grained services promise several beneficial characteristics for sustainable long-term software evolution…”
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A practical guide to multi-objective reinforcement learning and planning by Hayes, Conor F., Rădulescu, Roxana, Bargiacchi, Eugenio, Källström, Johan, Macfarlane, Matthew, Reymond, Mathieu, Verstraeten, Timothy, Zintgraf, Luisa M., Dazeley, Richard, Heintz, Fredrik, Howley, Enda, Irissappane, Athirai A., Mannion, Patrick, Nowé, Ann, Ramos, Gabriel, Restelli, Marcello, Vamplew, Peter, Roijers, Diederik M.

“…Real-world sequential decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the…”
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