01354nam0 2200265 i 450 SUN006971120140707111657.43620090504d1976 |0itac50 baitaIT|||| |||||ˆIl ‰chi è per il terzo mondo in Italiacensimento degli istituti di ricerca, dei centri di documentazione, dei comitati di solidarietà e degli organismi di volontariato che operano in Italia con riferimento al terzo mondo e ai problemi dello sviluppoa cura di Gian Carlo CostadoniRomaIPALMOstampa 197693 p.24 cm.001SUN00077822001 Atti e documentiIstituto superiore internazionale di scienze criminali9210 PadovaCEDAM1986-.SociologiaSGSUNC029784RomaSUNL000360Costadoni, Gian CarloSUNV054938IPALMOSUNV007584650ITSOL20181109RICASUN0069711UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE POLITICHE JEAN MONNET04 CONS VII.A.a.67 04 OMA1147 UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI SCIENZE POLITICHE JEAN MONNETOMA1147CONS VII.A.a.67caChi è" per il terzo mondo in Italia844346UNICAMPANIA03814nam 22006015 450 991048427740332120251113210203.03-030-38006-810.1007/978-3-030-38006-9(CKB)4900000000505018(MiAaPQ)EBC6011657(DE-He213)978-3-030-38006-9(PPN)243771649(EXLCZ)99490000000050501820200107d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAutomated Software Engineering: A Deep Learning-Based Approach /by Suresh Chandra Satapathy, Ajay Kumar Jena, Jagannath Singh, Saurabh Bilgaiyan1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (125 pages)Learning and Analytics in Intelligent Systems,2662-3455 ;83-030-38005-X Chapter 1: Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules -- Chapter 2: Effort Estimation of Web based Applications using ERD, use Case Point Method and Machine Learning -- Chapter 3: Usage of Machine Learning in Software Testing -- Chapter 4: Test Scenarios Generation using Combined Object-Oriented Models -- Chapter 5: A Novel Approach of Software Fault Prediction using Deep Learning Technique -- Chapter 6: Feature-Based Semi-Supervised Learning to Detect Malware from Android.This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering.Learning and Analytics in Intelligent Systems,2662-3455 ;8Computational intelligenceEngineeringData processingSoftware engineeringComputational IntelligenceData EngineeringSoftware EngineeringComputational intelligence.EngineeringData processing.Software engineering.Computational Intelligence.Data Engineering.Software Engineering.005.1Satapathy Suresh Chandraauthttp://id.loc.gov/vocabulary/relators/aut851467Jena Ajay Kumarauthttp://id.loc.gov/vocabulary/relators/autSingh Jagannathauthttp://id.loc.gov/vocabulary/relators/autBilgaiyan Saurabhauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910484277403321Automated Software Engineering: A Deep Learning-Based Approach2844493UNINA