Software reliability prediction using data abstraction and Random forest Algorithm

2021 ◽  
Author(s):  
Mayuri H. Molawade ◽  
Shashank. D. Joshi ◽  
Rohini Jadhav
Keyword(s):  
Random Forest ◽  
Software Reliability ◽  
Reliability Prediction ◽  
Data Abstraction ◽  
Random Forest Algorithm ◽  
Software Reliability Prediction ◽  
Using Data

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

A hierarchical mixture model for software reliability prediction

2007 ◽  
Vol 185 (2) ◽  
pp. 1120-1130 ◽  
Author(s):  
Shaoming Li ◽  
Qian Yin ◽  
Ping Guo ◽  
Michael R. Lyu
Keyword(s):  
Mixture Model ◽  
Software Reliability ◽  
Reliability Prediction ◽  
Software Reliability Prediction

Application of Machine Learning Techniques for Software Reliability Prediction (SRP)

2017 ◽  
pp. 113-142
Author(s):  
Pradeep Kumar
Keyword(s):  
Software Engineering ◽  
Software Reliability ◽  
Machine Learning Techniques ◽  
Reliability Prediction ◽  
Engineering Research ◽  
Reliability Models ◽  
Software Products ◽  
Software Reliability Prediction ◽  
Software Release ◽  
Software Engineering Research

Software reliability is a statistical measure of how well software operates with respect to its requirements. There are two related software engineering research issues about reliability requirements. The first issue is achieving the necessary reliability, i.e., choosing and employing appropriate software engineering techniques in system design and implementation. The second issue is the assessment of reliability as a method of assurance that precedes system deployment. In past few years, various software reliability models have been introduced. These models have been developed in response to the need of software engineers, system engineers and managers to quantify the concept of software reliability. This chapter on software reliability prediction using ANNs addresses three main issues: (1) analyze, manage, and improve the reliability of software products; (2) satisfy the customer needs for competitive price, on time delivery, and reliable software product; (3) determine the software release instance that is, when the software is good enough to release to the customer.

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