Machine-Learning-Assisted Software Defect Prediction / Najlacnejšie knihy
Machine-Learning-Assisted Software Defect Prediction

Kód: 49135032

Machine-Learning-Assisted Software Defect Prediction

Autor Xu, Zhou

This book focuses on software defect prediction (SDP) in order to avoid threats related to quality, reliability and safety. It details advanced machine/deep learning technologies to discuss strategies for identifying and preventin ... celý popis

205.79

Bežne: 222.90 €

Ušetríte 17.12 €


Skladom u dodávateľa
Odosielame za 10 - 13 dní
Pridať medzi želanie

Mohlo by sa vám tiež páčiť

Darčekový poukaz: Radosť zaručená
  1. Darujte poukaz v ľubovoľnej hodnote, a my sa postaráme o zvyšok.
  2. Poukaz sa vzťahuje na všetky produkty v našej ponuke.
  3. Elektronický poukaz si vytlačíte z e-mailu a môžete ho ihneď darovať.
  4. Platnosť poukazu je 12 mesiacov od dátumu vystavenia.

Objednať darčekový poukazViac informácií

Viac informácií o knihe Machine-Learning-Assisted Software Defect Prediction

Nákupom získate 498 bodov

Anotácia knihy

This book focuses on software defect prediction (SDP) in order to avoid threats related to quality, reliability and safety. It details advanced machine/deep learning technologies to discuss strategies for identifying and preventing such issues, and introduces innovative approaches to address feature irrelevance and redundancy, data imbalance in defect data, selection of representative module subsets for cross-version defect prediction, and managing data distribution variances in cross-project defect prediction.

The book is organized into eight chapters, systematically covering various aspects of software defect prediction.  First, chapter 1 Introduction explains the socio-economic significance and importance of software defect prediction. Next, chapter 2 Literature Review reviews and analyzes current technologies and their applications in defect prediction. Then chapter 3 Feature Learning discusses how to extract effective features from software engineering data using machine learning techniques. While chapter 4 Handling Class Imbalance introduces strategies to address the class imbalance in software defect data, chapter 5 Cross-Version Defect Prediction analyzes the application of historical version data to enhance the accuracy of prediction models. Subsequently, chapter 6 Cross-Project Defect Prediction discusses how to mitigate data discrepancies between projects through transfer learning, and chapter 7 Effort-Aware Defect Prediction delves into new technologies to rank software modules based on the defect density. Eventually, chapter 8 Conclusion and Future Trends summarizes the book and outlines future research directions.

The book mainly targets academic researchers and graduate students, particularly those focusing on the intersection of software engineering and machine learning. It is also intended for software engineers and data scientists working on enhancing the quality and safety of software.

Parametre knihy

Zaradenie knihy Knihy po nemecky Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Programmiersprachen

205.79



Osobný odber Bratislava a 12744 dalších

Copyright ©2008-26 najlacnejsie-knihy.sk Všetky práva vyhradenéSúkromieCookies


Môj účet: Prihlásiť sa
Všetky knihy sveta na jednom mieste. Navyše za skvelé ceny.

Nákupný košík ( prázdny )

Vyzdvihnutie v Zásielkovni
zadarmo nad 59,99 €.

Nachádzate sa: