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Easterbrook, S. Selecting empirical methods for software engineering research.

1st Edition

In Guide to advanced empirical software engineering. Springer, pp. Ekanayake, J. Tracking concept drift of software projects using defect prediction quality. Elberzhager, F. A systematic mapping study on the combination of static and dynamic quality assurance techniques.


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Fenton, N. A critique of software defect prediction models.


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  • Table of Contents;

Ganesan, K. Case-based software quality prediction. Gao, K. An empirical investigation of filter attribute selection techniques for software quality classification. Garousi, V. A systematic mapping study of web application testing.

Volume 118. Durable Phase-Change Memory Architectures

Gayatri, N. Performance analysis of datamining algorithms for software quality prediction. Gokhale, S. Regression tree modeling for the prediction of software quality. International Conference on Reliability and Quality in Design, pp. On the reproducibility of empirical software engineering studies based on data retrieved from development repositories. Empirical Software Engineering, Vol.

Guo, P. Software quality prediction using mixture models with EM algorithms. Gupta, D. Analysis of clustering techniques for software quality prediction. Hmood, A. Analyzing and predicting software quality trends using financial patterns. Hribar, L. Software component quality prediction using KNN and Fuzzy logic. ISBSG International Software Benchmarking Standards Group. Jiang, Y. Comparing design and code metrics for software quality prediction. Jin, C. Quality prediction model of object-oriented software system using computational intelligence.

Jorgensen, M. A systematic review of software development cost estimation studies. Kanmani, S.

Advances in Computers, Volume 29

Object oriented software quality prediction using general regression neural networks. Karg, L. A systematic literature review of software quality cost research. Kaur, A. Early software fault prediction using real time defect data. Kaur, D. A clustering algorithm for software fault prediction. International Conference on Computer and Communication Technology, pp.

Representative Research Publications

Keivanloo, I. A linked data platform for mining software repositories. Towards sharing source code facts using linked data. Khoshgoftaar, T.

SIAM Journal on Computing

Count models for software quality estimation. A multi-objective software quality classification model using genetic programming.

Using process history to predict software quality. Computer, Vol. Data mining for predictors of software quality. A multiobjective module-order model for software quality enhancement. Tree-based software quality estimation models for fault prediction. Fault prediction modeling for software quality estimation: Comparing commonly used techniques. Software quality estimation with case-based reasoning. Elsevier, pp. Kitchenham, B. A systematic review of systematic review process research in software engineering. Software quality: The elusive target. Landis, J. The measurement of observer agreement for categorical data.

Biometrics, Vol. Lewis Nigel, D. Liu, Y. Evolutionary optimization of software quality modeling with multiple repositories. Loh, C. Predicting quality of object-oriented systems through a quality model based on design metrics and data mining techniques. Lounis, H. Predicting quality attributes via machine-learning algorithms. Lyu, M. The increasing expansion of the application domain of parallel computing, as well as the development and introduction of new technologies and methodologies are covered in the Advances in Parallel Computing book series.

The series publishes research and development results on all aspects of parallel computing. Subscription info. Advances in Parallel Computing. Lucio Grandinetti, Gerhard R.

Advances in Computers, Volume 2 - PDF Free Download

Show contents. Parallel Computing is Everywhere. Joubert, Frans Peters. Deep Learning for Image Processing Applications. View all volumes in this series: Advances in Food Research.

Advances in Computers Volume 101

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