Improved forecasting model of software testing

doi:10.3778/j.issn.1002-8331.2010.31.018

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (31): 64-67.DOI: 10.3778/j.issn.1002-8331.2010.31.018

• 研发、设计、测试 • Previous Articles Next Articles

Improved forecasting model of software testing

XIE Min¹，REN Ping-an^1，2，MA Jian-feng²

1.College of Computer Science，Shaanxi Normal University，Xi’an 710062，China
2.College of Computer Science，Xidian University，Xi’an 710071，China

Received:2010-01-14 Revised:2010-05-20 Online:2010-11-01 Published:2010-11-01
Contact: XIE Min

改进的软件测试预测模型

解敏¹，任平安^1，2，马建峰²

1.陕西师范大学计算机科学学院，西安 710062
2.西安电子科技大学计算机科学学院，西安 710071

通讯作者: 解敏

Abstract

Abstract: Software testing prediction based on Gompertz model is poor fitness，this paper presents a novel model，in which the original Gompertz equation is expanded with the Taylor series by the Gauss-Newton method.After the initial value of parameters and sampling are chosen，the least squares method is used to calibrate parameters iteratively to estimate the parameters more accurately.The computing result with the actual data shows that this model improves the accuracy rate of defection detection，and that the absolute error rate in this model is less than in the original model.The computing result also shows that the fitness with the actual data in this model is better than in the original model.

Key words: software testing, improved Gompertz model, bug, forecasting

摘要： 基于Gompertz模型进行软件测试预测存在数据拟合精度差的不足，提出改进的Gompertz模型，用高斯-牛顿法将原Gompertz方程式按照泰勒级数展开，选取样本观测值与参数的初始点后，为了进一步估算参数，用最小二乘法反复迭代修正参数校正量。用实测数据验证改进后的模型，结果表明改进后的模型在缺陷发现的准确率、与实际数据的拟合度、预测值与实际值的绝对误差率方面都优于原模型。

关键词: 软件测试, 改进Gompertz模型, 缺陷, 预测

CLC Number:

TP311.5

XIE Min¹，REN Ping-an^1，2，MA Jian-feng². Improved forecasting model of software testing[J]. Computer Engineering and Applications, 2010, 46(31): 64-67.

解敏¹，任平安^1，2，马建峰². 改进的软件测试预测模型[J]. 计算机工程与应用, 2010, 46(31): 64-67.

[1]	ZHANG Qianyu, YAN Dongmei, HAN Jiatong. Research on Stock Price Prediction Combined with Deep Learning and Decomposition Algorithm [J]. Computer Engineering and Applications, 2021, 57(5): 56-64.
[2]	ZHAO Hongrui, XUE Lei. Research on Stock Forecasting Based on LSTM-CNN-CBAM Model [J]. Computer Engineering and Applications, 2021, 57(3): 203-207.
[3]	LOU Qihe, LIU Hu, XIE Xiangying, MA Xiaoguang. Short-Term Power Load Forecasting Using Cascaded LSTM Models [J]. Computer Engineering and Applications, 2021, 57(18): 275-280.
[4]	DANG Jianwu, CONG Xiaoqing. Research on Hybrid Stock Index Forecasting Model Based on CNN and GRU [J]. Computer Engineering and Applications, 2021, 57(16): 167-174.
[5]	TAN Lijuan, ZHENG Wei, LIU Youlin, FAN Xin, YANG Fengyu. Survey of Airborne Software Test and Verification Methods for Airworthiness Standards [J]. Computer Engineering and Applications, 2021, 57(15): 9-22.
[6]	LIU Youlin, ZHENG Wei, TAN Lijuan, FAN Xin, YANG Fengyu. Summary of Airborne Software Testing and Verification Tools for Airworthiness Standards [J]. Computer Engineering and Applications, 2021, 57(11): 1-10.
[7]	DENG Xuankun1，2, WAN Liang1，2, DING Hongwei1，2, XIN Zhuang1，2. Traffic Flow Prediction Based on Deep Neural Networks [J]. Computer Engineering and Applications, 2019, 55(2): 228-235.
[8]	LU Yutian, WANG Xiaoyi, WANG Li, XU Jiping, BAI Yuting. Forecasting Method for Industrial Exhaust Gas Based on Compound Nonlinear Auto Regressive Neural Network [J]. Computer Engineering and Applications, 2019, 55(18): 223-228.
[9]	LU Wenxing, LI Chu. Forecasting of Short-Time Tourist Flow Based on Improved PSO Algorithm Optimized LSSVM Model [J]. Computer Engineering and Applications, 2019, 55(18): 247-255.
[10]	QIU Yihao, MENG Zhiqing. Temporal Neural Network Model and Its Application of Stock Classification Prediction [J]. Computer Engineering and Applications, 2019, 55(15): 263-270.
[11]	LIU Wenjie, JIANG He. Analysis of Software Bug Reports Severity Feature [J]. Computer Engineering and Applications, 2019, 55(14): 48-53.
[12]	LI Mei1，2, NING Dejun1, GUO Jiacheng1，2. Attention Mechanism-Based CNN-LSTM Model and Its Application [J]. Computer Engineering and Applications, 2019, 55(13): 20-27.
[13]	YAN Hongwen, LU Geyu. Application research on complete ensemble empirical mode decomposition, wavelet transform and convolutional neural networks in short-term wind speed forecasting [J]. Computer Engineering and Applications, 2018, 54(9): 224-230.
[14]	WANG Ying. Software testing with uncertain requirements [J]. Computer Engineering and Applications, 2018, 54(20): 35-41.
[15]	XUE Meng, JIANG Shujuan, WANG Rongcun. Systematic review of test data generation based on intelligent optimization algorithm [J]. Computer Engineering and Applications, 2018, 54(17): 16-23.

Improved forecasting model of software testing

改进的软件测试预测模型

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics