Long-Term Software Fault Prediction Model with Linear Regression and Data Transformation

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Begum Momotaz; Rony Jahid Hasan; Islam Md. Rashedul; Uddin Jia

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Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2023 | Volume:11 | Issue:43 Page(s): 222-231

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Information Journal Paper

Title

Long-Term Software Fault Prediction Model with Linear Regression and Data Transformation

Author(s)

Begum Momotaz | Rony Jahid Hasan | Islam Md. Rashedul | Uddin Jia | Issue Writer Certificate

Keywords

Software ReliabilityQ4

Software FaultsQ4

ForecastingQ4

Long Term PredictionQ4

Relative ErrorQ4

Abstract

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.

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