Service-based software systems (SBSSs) are widely deployed due to the growing trend of distributed computing and cloud computing. It is important to ensure high quality of an SBSS, especially in a strongly competitive market. Existing works on SBSS reliability usually assumed independence of service failures. However, the fact that resource sharing exists in different levels of SBSS operations invalidates this assumption. Ignorance of failure dependencies have been discussed as potentially affecting system reliability predictions and lowering the benefits of design diversity, as typically seen in high-reliability systems. In this paper, we propose a reliability framework that incorporates failure dependence modeling, system reliability modeling, as well as reliability analysis for individual services and for failure sources. The framework is also capable of analyzing the internal structures of popular software fault tolerant (FT) schemes. The proposed method is applied to a travel agency system based upon a real-world practice for verifying its accuracy of reliability modeling and effectiveness of varied reliability measures. The results show that failure dependence of the services is an essential factor for analyzing any valuable SBSS system. Further, a set of reliability measures with different capabilities and complexities are available for assisting SBSS engineers with system improvements.

Summary Presently, service‐based software systems (SBSSs) have been heavily deployed to fulfil the functionalities of cloud computing and are widely used in many other application fields. Additionally, maintaining functionality and quality of service levels becomes increasingly important for SBSSs; this is because system operational failures may cause great financial loss to an organization. Fault tolerance (FT) is usually used to provide continuous and reliable system service delivery when failures occur. However, the reliability and performance of FT should be carefully analysed because of the overhead of invoking redundant services. It is also noted that the single point of failure on the FT adjudicators as well as the failure correlation also hamper the benefits of FT in SBSSs. To address these problems, this paper proposes two approaches, the stochastic modelling approach and the simulation approach, for analysing the reliability and performance of generalized FT designs. The first approach is suitable for quick analysis at an early design stage, while the second approach is built on top of the ns‐3 simulator and could be well adapted to incorporate varied uncertainty models in the SBSS environments Extensive experiments and analyses uncover some characteristics that could be useful for SBSS engineers. Copyright © 2015 John Wiley & Sons, Ltd.

During the process of fault localization, the spectrum-based techniques are frequently used and widely studied since they can automatically and effectively localize the faults of software and be implemented easily. So far most of spectrum-based fault localization techniques have relied heavily on the use of similarity coefficients. However, we noticed that existing similarity coefficients for fault localization may lack measure(s) to properly reflect the relationship between failing and passing test cases. It also has to note that the failing test cases usually are expected to provide more information to the similarity coefficients than the passing test cases. In order to evaluate the importance of failing and passing test cases in the similarity coefficients, a number of modified similarity coefficients in fault localization are presented and discussed. The modified similarity coefficients which are assigned the weights of the failing and/or passing test cases will be studied and analyzed with the multi-coverage-combined techniques. Five open source programs and 75 faulty versions in total from Siemens suite, which have been widely used for software testing and comparison of fault localization techniques, were selected as experiment subjects. Detailed analysis of the results shows that assigning the weights of failing and passing test cases to the similarity coefficients would be able to localize the faults more effectively and accurately.