Software Reliability Growth Model for Imperfect Debugging Process Considering Testing-Effort and Testing Coverage

Because of the inevitable debugging lag,imperfect debugging process is used to replace perfect debugging process in the analysis of software reliability growth model.Considering neither testing-effort nor testing coverage can describe software reliability for imperfect debugging completely,by hybridizing testing-effort with testing coverage under imperfect debugging,this paper proposes a new model named GMW-LO-ID.Under the assumption that the number of faults is proportional to the current number of detected faults,this model combines generalized modified Weibull(GMW)testing-effort function with logistic(LO)testing coverage function,and inherits GMW's amazing flexibility and LO's high fitting precision.Furthermore,the fitting accuracy and predictive power are verified by two series of experiments and we can draw a conclusion that our model fits the actual failure data better and predicts the software future behavior better than other ten traditional models,which only consider one or two points of testing-effort,testing coverage and imperfect debugging.

A Fault-Based Testing Approach in Safety Critical Medical Systems

The advent of technology has opened unprecedented opportunities in health care delivery system as the demand for intelligent and knowledge-based systems has increased as modern medical practices become more knowledge-intensive. As a result of this, there is greater need to investigate the pervasiveness of software faults in Safety critical medical systems for proper diagnosis. The sheer volume of code in these systems creates significant concerns about the quality of the software. The rate of untimely deaths nowadays is alarming partly due to the medical device used to carry out the diagnosis process. A safety-critical medical (SCM) system is a complex system in which the malfunctioning of software could result in death, injury of the patient or damage to the environment. The malfunctioning of the software could be as a result of the inadequacy in software testing due to test suit problem or oracle problem. Testing a SCM system poses great challenges to software testers. One of these challenges is the need to generate a limited number of test cases of a given regression test suite in a manner that does not compromise its defect detection ability. This paper presents a novel five-stage fault-based testing procedure for SCM, a model-based approach to generate test cases for differential diagnosis of Tuberculosis. We used Prime Path Coverage and Edge-Pair Coverage as coverage criteria to ensure maximum coverage to identify feasible paths. We analyzed the proposed testing procedure with the help of three metrics consisting of Fault Detection Density, Fault Detection Effectiveness and Mutation Adequacy Score. We evaluated the effectiveness of our testing procedure by running the suggested test cases on a sample historical data of tuberculosis patients. The experimental results show that our developed testing procedure has some advantages such as creating mutant graphs and Fuzzy Cognitive Map Engine while resolving the problem of eliminating infeasible test cases for effective decision making.

Planning failure-censored constant-stress partially accelerated life test

This article deals with the case of the failure-censored constant-stress partially accelerated life test （CSPALT） for highly reliable materials or products assuming the Pareto distribution of the second kind. The maximum likelihood （ML） method is used to estimate the parameters of the CSPALT model. The performance of ML estimators is investigated via their mean square error. Also, the average confidence interval length （IL） and the associated co- verage probability （CP） are obtained. Moreover, optimum CSPALT plans that determine the optimal proportion of the test units al- located to each stress are developed. Such optimum test plans minimize the generalized asymptotic variance （GAV） of the ML estimators of the model parameters. For illustration, Monte Carlo simulation studies are given and a real life example is provided.

Iterative Android automated testing

With the benefits of reducing time and workforce,automated testing has been widely used for the quality assurance of mobile applications(APPs).Compared with automated testing,manual testing can achieve higher coverage in complex interactive Activities.And the effectiveness of manual testing is highly dependent on the user operation process(UOP)of experienced testers.Based on the UOP,we propose an iterative Android automated testing(IAAT)method that automatically records,extracts,and integrates UOPs to guide the test logic of the tool across the complex Activity iteratively.The feedback test results can train the UOPs to achieve higher coverage in each iteration.We extracted 50 UOPs and conducted experiments on 10 popular mobile APPs to demonstrate IAAT’s effectiveness compared with Monkey and the initial automated tests.The experimental results show a noticeable improvement in the IAAT compared with the test logic without human knowledge.Under the 60 minutes test time,the average code coverage is improved by 13.98%to 37.83%,higher than the 27.48%of Monkey under the same conditions.

Customer Activity Sequence Classification for Debt Prevention in Social Security

From a data mining perspective, sequence classification is to build a classifier using frequent sequential patterns. However, mining for a complete set of sequential patterns on a large dataset can be extremely time-consuming and the large number of patterns discovered also makes the pattern selection and classifier building very time-consuming. The fact is that, in sequence classification, it is much more important to discover discriminative patterns than a complete pattern set. In this paper, we propose a novel hierarchical algorithm to build sequential classifiers using discriminative sequential patterns. Firstly, we mine for the sequential patterns which axe the most strongly correlated to each target class. In this step, an aggressive strategy is employed to select a small set of sequential patterns. Secondly, pattern pruning and serial coverage test are done on the mined patterns. The patterns that pass the serial test are used to build the sub-classifier at the first level of the final classifier. And thirdly, the training samples that cannot be covered are fed back to the sequential pattern mining stage with updated parameters. This process continues until predefined interestingness measure thresholds are reached, or all samples axe covered. The patterns generated in each loop form the sub-classifier at each level of the final classifier. Within this framework, the searching space can be reduced dramatically while a good classification performance is achieved. The proposed algorithm is tested in a real-world business application for debt prevention in social security area. The novel sequence classification algorithm shows the effectiveness and efficiency for predicting debt occurrences based on customer activity sequence data.