Software is currently a key part of many safety-critical and life-critical application systems. People always need easy- and instinctive-to-use software, but the biggest challenge for software engineers is how to develop software with high reliability in a timely manner. To assure quality, and to assess the reliability of software products, many software reliability growth models (SRGMs) have been proposed in the past three decades. The practical problem is that sometimes these selected SRGMs by companies or software practitioners disagree in their reliability predictions, while no single model can be trusted to provide consistently accurate results across various applications. Consequently, some researchers have proposed to use combinational models for improving the prediction capability of software reliability. In this paper, three enhanced weighted-combinations, namely weighted arithmetic, weighted geometric, and weighted harmonic combinations, are proposed. To solve the problem of determining proper weights for model combinations, we further study how to incorporate enhanced genetic algorithms (EGAs) with several efficient operators into weighted assignments. Experiments are performed based on real software failure data, and numerical results show that our proposed models are flexible enough to depict various software development environments. Finally, some management metrics are presented to both assure software quality and determine the optimal release strategy of software products under development.

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.

With the growing size and complexity of software applications, traditional software reliability methods are insufficient to analyze inter-component interactions of modular software systems. The number of test cases may be extremely large for this application; therefore, it is hard for us to extensively test each software component given resource limitations. In this paper, we propose an adaptive framework of incorporating path testing into reliability estimation for modular software systems. Three estimated methods based on common program structures, namely, sequence, branch, and loop structures, are proposed to calculate the path reliability. Consequently, the derived path reliabilities can be applied to the estimates of software reliability. Some experiments are performed based on two real systems. In addition, the accuracy and correlation with respect to the experiments are investigated by simulation and sensitivity analysis. Experimental results show that the path reliability has a high correlation to the actual software reliability. For software with loop structures, a smaller loop number can be assigned to derive an acceptable estimation of path reliability. Further, the sensitivity analysis can be used to identify critical modules and paths for resource allocation. It can be concluded that the proposed methods are useful and helpful for estimating software reliability and can be adaptively used in the early stages of software development.

In order to assure software quality and assess software reliability, many software reliability growth models (SRGMs) have been proposed for estimation of reliability growth of products in the past three decades. In principle, two widely used methods for the parameter estimation of SRGMs are the maximum likelihood estimation (MLE) and the least squares estimation (LSE). However, the approach of these two estimations may impose some restrictions on SRGMs, such as the existence of derivatives from formulated models or the needs for complex calculation. Thus in this paper, we propose a modified genetic algorithm (MGA) to estimate the parameters of SRGMs. Experiments based on real software failure data are performed, and the results show that the proposed genetic algorithm is more effective and faster than traditional genetic algorithms.

In the past, some researches suggested that engineers can use combined software reliability growth models (SRGMs) to obtain more accurate reliability prediction during testing. In this paper, three weighted combinational models, namely, equal, linear, and nonlinear weight, are proposed for reliability estimation of web-based software. We further investigate the estimation accuracy of using genetic algorithm to determine the weight assignment for the proposed models. Preliminary result shows that the linearly and nonlinearly weighted combinational models have better prediction capability than single SRGM and equally weighted combinational model for web-based software.

The uses of the World Wide Web on the Internet for commerce and information access continue to expand. The e-commerce business has proven to be a promising channel of choice for consumers as it is gradually transforming into a mainstream business activity. However, lack of trust has been identified as a major obstacle to the adoption of online shopping. Empirical study of online trust is constrained by the shortage of high-quality measures of general trust in the e-commence contexts. Based on theoretical or empirical studies in the literature of marketing or information system, nine factors have sound theoretical sense and support from the literature. A survey method was used for data collection in this study. A total of 172 usable questionnaires were collected from respondents. This study presents a new set of instruments for use in studying online trust of an individual. The items in the instrument were analyzed using a factors analysis. The results demonstrated reliable reliability and validity in the instrument.This study identified seven factors has a significant impact on online trust. The seven dominant factors are reputation, third-party assurance, customer service, propensity to trust, website quality, system assurance and brand. As consumers consider that doing business with online vendors involves risk and uncertainty, online business organizations need to overcome these barriers. Further, implication of the finding also provides e-commerce practitioners with guideline for effectively engender online customer trust.

In this paper, we propose a modified genetic algorithm (MGA) with calibrating fitness functions, weighted bit mutation, and rebuilding mechanism for the parameter estimation of software reliability growth models (SRGMs). An example using a real failure data is given to demonstrate the performance of proposed method. Experimental result shows that MGA is effective for estimating the parameters of SRGM.

Grey relational analysis (GRA), a similarity-based method, presents acceptable prediction performance in software effort estimation. However, we found that conventional GRA methods only consider non-weighted conditions while predicting effort. Essentially, each feature of a project may have a different degree of relevance in the process of comparing similarity. In this paper, we propose six weighted methods, namely, non-weight, distance-based weight, correlative weight, linear weight, nonlinear weight, and maximal weight, to be integrated into GRA. Three public datasets are used to evaluate the accuracy of the weighted GRA methods. Experimental results show that the weighted GRA performs better precision than the non-weighted GRA. Specifically, the linearly weighted GRA greatly improves accuracy compared with the other weighted methods. To sum up, the weighted GRA not only can improve the accuracy of prediction but is an alternative method to be applied to software development life cycle.