LEADER 03814nam 22006015 450 001 9910484277403321 005 20251113210203.0 010 $a3-030-38006-8 024 7 $a10.1007/978-3-030-38006-9 035 $a(CKB)4900000000505018 035 $a(MiAaPQ)EBC6011657 035 $a(DE-He213)978-3-030-38006-9 035 $a(PPN)243771649 035 $a(EXLCZ)994900000000505018 100 $a20200107d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAutomated Software Engineering: A Deep Learning-Based Approach /$fby Suresh Chandra Satapathy, Ajay Kumar Jena, Jagannath Singh, Saurabh Bilgaiyan 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (125 pages) 225 1 $aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v8 311 08$a3-030-38005-X 327 $aChapter 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. 330 $aThis book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software?s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering. 410 0$aLearning and Analytics in Intelligent Systems,$x2662-3455 ;$v8 606 $aComputational intelligence 606 $aEngineering$xData processing 606 $aSoftware engineering 606 $aComputational Intelligence 606 $aData Engineering 606 $aSoftware Engineering 615 0$aComputational intelligence. 615 0$aEngineering$xData processing. 615 0$aSoftware engineering. 615 14$aComputational Intelligence. 615 24$aData Engineering. 615 24$aSoftware Engineering. 676 $a005.1 700 $aSatapathy$b Suresh Chandra$4aut$4http://id.loc.gov/vocabulary/relators/aut$0851467 702 $aJena$b Ajay Kumar$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aSingh$b Jagannath$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aBilgaiyan$b Saurabh$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484277403321 996 $aAutomated Software Engineering: A Deep Learning-Based Approach$92844493 997 $aUNINA