Web service composition is the process of combining and reusing existing web services to create new business processes to satisfy specific user requirements. Reliability plays an important role in ensuring the quality of web service composition. However, owing to the flexibility and complexity of such architecture, sufficient estimation of reliability is difficult. In this paper, the authors propose a method to estimate the reliability of web service compositions based on Bayes reliability assessment by considering it to be a decision-making problem. This improves the testing efficiency and accuracy of such methods. To this end, the authors focus on fully utilizing prior information of web services to increase the accuracy of prior distributions, and construct a Markov model in terms of the reliabilities of the web composition and each web service to integrate the limited test data. The authors further propose a method of minimum risk (MMR) to calculate the initial values of hyperparameters satisfying the constraint of minimal risk of the wrong decision. Experiments demonstrate that the proposed method is capable of efficiently utilizing prior module-level failure information, comparing with the Bayesian Monte Carlo method (BMCM) and expert scoring method (ESM), when the number of failures increased from 0 to 5, reducing the required number of test cases from 19.8% to 28.9% and 6.1% to 14.1% separately, improving the reliability assessment of web service compositions, and reducing the expenses incurred by system-level reliability testing and demonstration.

Abstract. Code defect detection technology plays an increasingly important role in software testing, but the problem of large number of alarms and high false positive rate is common, thus, the efficiency and difficulty of manual confirmation has seriously hindered the development of this technology. This paper proposes a generation method of dominant alarm, which can effectively reduce the number of the human confirmations and improve validation efficiency. Firstly, by analyzing the feature function of the alarms, the alarms with the same data source can be classified as a collection called 'Equivalent class collection'. Then, the dominant alarm in the equivalent class collection is determined by examining the contextual relationship of the codes where the alarm exits in the collection. Finally, the confirmation of the alarms in the collection can be accomplished by confirming the dominant alarm only. The experiment results show that this method can effectively improve the human conformation efficiency of 20%-30%.

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