Mendeley
TY  _  JOUR
ID  -  2022041336585
TI  -  Long-Term Software Fault Prediction Model with Linear Regression and Data Transformation
JO  -  Journal of Information Systems and Telecommunication (JIST) 
JA  -  ES
LA  -  en
SN  -  2322-1437
AU  -  Begum Momotaz
AU  -  Rony Jahid Hasan
AU  -  Islam Md. Rashedul
AU  -  Uddin Jia
AD  -  Woosong University
AD  -  Dhaka University of Engineering & Technology
AD  -  Dhaka University of Engineering & Technology, Gazipur-1707, Dhaka, Bangladesh;
AD  -  International University of Business Agriculture and Technology
Y1  -  2023
PY  -  2023
VL  -  43
IS  -  11
SP  -  222
EP  -  231
KW  -  Software Reliability
KW  -   Software Faults
KW  -   Forecasting
KW  -   Long Term Prediction
KW  -   Relative Error
DO  -  
N2  -  The validation performance is obligatory to ensure the software reliability by determining the characteristics of an implemented software system. To ensure the reliability of software, not only detecting and solving occurred faults but also predicting the future fault is required. It is performed before any actual testing phase initiates. As a result, various works on software fault prediction have been done. In this paper presents, we present a software fault prediction model where different data transformation methods are applied with Poisson fault count data. For data pre-processing from Poisson data to Gaussian data, Box-Cox power transformation (Box-Cox_T), Yeo-Johnson power transformation (Yeo-Johnson_T), and Anscombe transformation (Anscombe_T) are used here. And then, to predict long-term software fault prediction, linear regression is applied. Linear regression shows the linear relationship between the dependent and independent variable correspondingly relative error and testing days. For synthesis analysis, three real software fault count datasets are used, where we compare the proposed approach with Naïve gauss, exponential smoothing time series forecasting model, and conventional method software reliability growth models (SRGMs) in terms of data transformation (With_T) and non-data transformation (Non_T). Our datasets contain days and cumulative software faults represented in (62, 133), (181, 225), and (114, 189) formats, respectively. Box-Cox power transformation with linear regression (L_Box-Cox_T) method, has outperformed all other methods with regard to average relative error from the short to long term.
UR  - rimag.ir/fa/Article/36585
L1  - rimag.ir/fa/Article/Download/36585
ER  -  
