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It has been proved by practice that mining weblogs to detect software errors is an effective software testing method. This paper presents a software failure detection method based on the incremental mining weblog strategy and gives data mining steps for the implementation of this method. A case is studied for the proposed test method. In this case, we use Splunk, a data analysis tool, to analyze some weblogs that record some linked information of mobile applications for android. The result of the data analysis shows that the proposed method can effectively detect the software failure problem in the download process of mobile applications. Therefore, the proposed method can be used for software reliability assessment. [ABSTRACT FROM AUTHOR]

With the widespread application of intelligent software, more rigorous requirements are placed on the security and reliability of intelligent software programs. The major challenge is that the traditional test approaches cannot be easily adapted to the testing of intelligent software since the test oracle is not available and testing intelligent software needs to focus on training sets. The classifier is a typical intelligent software which has an uncertain output. As a result, the accuracy rate cannot be used to judge whether the classifier is defective. Therefore, this paper proposes an approach for testing classifiers based on compositional metamorphic relations. Initially, it was recommended to construct composite metamorphic relations to generate derivative test cases from the original test cases; followed by training classifier to predict the classification of the test data set; then checks their consistency between the original test cases and the derivative test cases against compositional metamorphic relations, and the detected violations are reported as bugs. The experiment is carried on the ID3 decision tree and compares the mutant detection capability with the on one-dimensional metamorphic testing. From the results received from the experiment conducted in this research shows that the proposed approach improves 16.7% of mutant kill rates. [ABSTRACT FROM AUTHOR]

Critical systems are becoming increasingly software intensive, necessitating reliable software to ensure proper operation. Nonhomogeneous Poisson process software reliability growth models are commonly used to characterize fault detection as a function of testing time, which enables quantitative assessment of software reliability. Many early models assumed that the testing-effort was constant throughout software testing. To remove this assumption, researchers have proposed models incorporating testing-effort, yet this significantly increases model complexity to the degree that most previous studies utilized a two-step procedure involving least squares estimation (LSE) and algorithms, including Newton's method to estimate the parameters of a testing-effort model. This approach may limit the quality of the model fit achieved. Moreover, the research trend over the past 30 years has been to propose progressively more complex models, sacrificing practical considerations such as predictive accuracy. This paper proposes a two-step procedure that utilizes the expectation conditional maximization (ECM) algorithm, referred to as the ECM/ECM approach, to obtain the parameter estimates of a software reliability growth model incorporating testing-effort. The results of the proposed approach are compared to past methods as well as a simpler model that does not consider testing-effort to assess whether the additional complexity introduced by testing-effort functions compromises predictive accuracy. Our results indicate that the ECM/ECM approach achieves a better goodness of fit with respect to four measures, including three predictive measures. In some cases, the simpler model omitting testing-effort outperforms methods considering testing-effort. These results suggest that the proposed ECM/ECM approach can achieve better parameter estimates than the previously proposed LSE/MLE approach and that algorithms to improve fit and predictive accuracy may better serve users of software reliability models. [ABSTRACT FROM AUTHOR]

Nowadays, aiming at the problem of long testing duration of software reliability demonstration testing, this paper presents a method that can greatly shorten testing duration. First, the idea of a monotone decreasing function is integrated into the Bayesian theory of software reliability demonstration testing, and then a software reliability demonstration testing scheme of a Bayesian method based on monotone decreasing function is proposed. Secondly, according to the real-time prior information in the testing phase, priori information is dynamically integrated. Two kinds of priori dynamic integration methods suitable for two different conditions are proposed. Finally, through experimental analysis, it is proven that the testing scheme can significantly reduce testing duration under the same confidence condition, and the two kinds of prior dynamic integration methods are feasible. [ABSTRACT FROM AUTHOR]

Defect prediction is meaningful because it can assist software inspection by predicting defective code locations and improving software reliability. Many software features are designed for defect prediction models to identify potential bugs, but no one feature set can perform well in most cases yet. To improve defect prediction, this paper proposes a new code feature, the cross-entropy value of the sequence of code's abstract syntax tree nodes (CE-AST), and develops a neural language model for feature measurement. To evaluate the effectiveness of CE-AST, we first investigate its discrimination for defect-proneness. Experiments on 12 Java projects show that CE-AST is more discriminative than 45% of twenty widely used traditional features. Furthermore, we investigate CE-AST's contribution to defect prediction. Combined with different traditional feature suites to feed prediction models, CE-AST can bring performance improvements of 4.7% in Precision, 2.5% in Recall, and 3.5% in F1 on average. [ABSTRACT FROM AUTHOR]

The non-homogeneous Poisson processes (NHPPs) based software reliability models (SRMs) have gained much popularity in actual software testing phases to assess the software reliability, the number of remaining software faults and the software release scheduling. It is well known that the Weibull distribution plays an important role in reliability applications because of its flexibility in being able to represent various patterns of failure rate functions. In this paper, we introduce some recent generations of Weibull distribution to represent the underlying software fault-detection time distribution of the NHPP-based SRMs. We study the effectiveness of Weibull-type distributions in software reliability modeling through goodness-of-fit test and prediction analysis. [ABSTRACT FROM AUTHOR]

Most Software Reliability Growth Models (SRGMs) ignore either the learning phenomenon of the testing team or quality of debugging or both for the sake of simplicity. Some SRGMs consider imperfect debugging tightly integrated with the learning phenomenon. In practice, imperfect debugging and the learning phenomenon of the testing team are independent of each other since the former indicates quality of debugging process and the latter the quality of the testing process, usually carried out by different teams. Furthermore, sometimes the debugging team may find and correct some more faults in addition to the faults which caused the failures leading to what is known as efficient debugging. In this paper, a new mathematically derived SRGM is proposed, which also provides estimates of various quality metrics such as the number of faults in the software system, debugging and the learning indices. The proposed SRGM seems to describe the time to failure data of many software projects satisfactorily. [ABSTRACT FROM AUTHOR]

This paper considers a software development scenario where a software development team develops, tests and releases software version by version. A modeling framework is proposed to study the expected number of remaining faults in each version. The optimal development time and testing time for each version are also studied. [ABSTRACT FROM AUTHOR]