Optimal stopping time of software system test via artificial neural network with fault count data

Abstract

Purpose The purpose of this paper is to present a novel method to estimate the optimal software testing time which minimizes the relevant expected software cost via a refined neural network approach with the grouped data, where the multi-stage look ahead prediction is carried out with a simple three-layer perceptron neural network with multiple outputs. Design/methodology/approach To analyze the software fault count data which follows a Poisson process with unknown mean value function, the authors transform the underlying Poisson count data to the Gaussian data by means of one of three data transformation methods, and predict the cost-optimal software testing time via a neural network. Findings In numerical examples with two actual software fault count data, the authors compare the neural network approach with the common non-homogeneous Poisson process-based software reliability growth models. It is shown that the proposed method could provide a more accurate and more flexible decision making than the common stochastic modeling approach. Originality/value It is shown that the neural network approach can be used to predict the optimal software testing time more accurately.

Data Protection Software for Civil Aviation Control Flight Information System Based on FPE Algorithm

“…) is a brand new cryptography technology; it takes the plaintext in a specific format, encrypted into ciphertext in the same format, that is, the type and length of the ciphertext are the same as the plaintext. FPE is a symmetric cipher that requires the ciphertext and plaintext to have the same format; its original intention was to encrypt the database or personally identifiable information (PII) in the application system and ensure that the ciphertext and plaintext of the information have the same data type and length; furthermore [18,19], it is possible to achieve the purpose of encryption and reduce costs without modifying the database structure or application program. is kind of good encryption idea does not stop at the above several applications but is constantly being explored by people.…”

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

“…Where, is the smoothing constant (0.25, 0.5, 0.75, 1) and y n is the predictive software fault at n-th testing days. Exponential smoothing is presented in equation (15).…”

“…However, the architecture of neural networks is complex; as a result, computation time is very high. The same author has related works [10][11][12][13] based on multilayer perceptron to address optimal software release problems. Furthermore, the paper [14] presented a neuro base software fault prediction method using the Box-Cox transformation scheme.…”

“…Subsequently, they compared their result with traditional SRGMs and showed that their method outperformed in the early testing phase. On the other hand, multiplayer neural network architecture is used for identifying optimal software testing time [15]. For underlying software fault count, they have also preprocessed the data using a well-known data transformation technique.…”

Software Defects Identification: Results Using Machine Learning and Explainable Artificial Intelligence Techniques

“…On the other hand, software release time heavily depends on the appropriate diagnosis of a software fault, which has become researchers' primary concern. Begum and Dohi [10], [11] presented various ANN strategies for identifying optimal software release time. The limitation of employing a multi-stage look-ahead prediction method in which the optimal number of hidden neurons and transformation values were determined.…”