TY  - JOUR
T1  - An Novel Approach On Software Reliability Growth Modelin Using the Data Mining Techniques
AU - Nandini, G. AU - Sridevi, G. 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 18
SP  - 3556
EP  - 3561
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3556.3561
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3556.3561
KW  - Software reliability
KW  -genetic programming
KW  -modeling
KW  -software faults
KW  -conferred
AB  - Software is directly a key part of many safety-critical and life-critical function systems. People
consistently need easy- and instinctive-to-use software but the colossal challenge for software engineers is
how to advance software with high accuracy in a appropriate manner in a appropriate manner. To assure
quality and to assess the authenticity of software products, many Software Reliability advance Models
(SRGMs) have been expected in the past three decades. The constructive problem is that consistently these
selected SRGMs by association or software professional disagree in their reliability forecast while no single
exemplary can be trusted to administer consistently accurate results across assorted applications.
Consequently, some investigator have expected to use combinational models for develop the prediction
capability of operating system reliability. In this study, appreciate weighted-combination, namely adequate
arithmetic combination are expected. To solve the dilemma of determining proper burden for model
combinations, we farther study how to assimilate Enhanced Genetic conclusion (EGAs) with several efficient
engineer into weighted assignments. analysis are performed based on absolute software breakdown data and
numerical conclusion show that our expected models are malleable enough to depict assorted software
development climate. Finally, some administration metrics are conferred to both assure software aspect and
complete the optimal release approach of software amount under development.
ER  -