Chao-Jung Hsu

An Adaptive Reliability Analysis Using Path Testing for Complex Component-Based Software Systems

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.

Comparison of weighted grey relational analysis for software effort estimation

In recent years, grey relational analysis (GRA), a similarity-based method, has been proposed and used in many applications. However, we found that most traditional GRA methods only consider nonweighted similarity for predicting software development effort. In fact, nonweighted similarity may cause biased predictions, because each feature of a project may have a different degree of relevance to the development effort. Therefore, this paper proposes six weighted methods, including nonweighted, distance-based, correlative, linear, nonlinear, and maximal weights, to be integrated into GRA for software effort estimation. Numerical examples and sensitivity analyses based on four public datasets are used to show the performance of the proposed methods. The experimental results indicate that the weighted GRA can improve estimation accuracy and reliability from the nonweighted GRA. The results also demonstrate that the weighted GRA performs better than other estimation techniques and published results. In summary, we can conclude that weighted GRA can be a viable and alternative method for predicting software development effort.

Improving Effort Estimation Accuracy by Weighted Grey Relational Analysis During Software Development

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.

A Study of Improving the Accuracy of Software Effort Estimation Using Linearly Weighted Combinations

An accurate prediction of software effort has been the goal of successful software project management for more than thirty years. In order to achieve this goal, many software effort estimation methods have been proposed. Unfortunately, none of these methods developed thus far have been able to offer consistent prediction accuracy in all cases. In this paper, therefore, we integrate several software effort estimation methods and assign linear weights for combinations. It is noted that the weight assignment is based on the outcome of single methods. Seven public datasets and three criteria are used to evaluate the accuracy of our combinational models. Experimental results show that the proposed combination models are a useful method for improving estimation accuracy.

Reliability analysis using weighted combinational models for web-based software

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.

Optimal Weighted Combinational Models for Software Reliability Estimation and Analysis

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.

A Study on the Applicability of Modified Genetic Algorithms for the Parameter Estimation of Software Reliability Modeling

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.

Comparison and Assessment of Improved Grey Relation Analysis for Software Development Effort Estimation

The goal of software project planning is to provide a framework that allows project manager to make reasonable estimates of the resources. In fact, software development is highly unpredictable - only 10% of projects on time and budget. Thus, it is very important for software project managers to accurately and precisely estimate software development effort since the resources are limited. One of the most widely used approaches of software effort estimation is the analogy method. Since the method of analogy is constructed on the foundation of distance-based similarity, there are still some drawbacks and restrictions for application. For example, the anomalistic and outlying values will influence the function to determine similarity. Contrarily, grey relational analysis (GRA) is a distinct measurement from the traditional distance scale and can dig out the realistic law from small-sample data. In this paper, we show how to apply GRA to evaluate the effort estimation results for different data sequences and to compare its accuracy with that of Analogy method. Experimental result shows that the GRA provides a better predictive performance than other methods. We can see that the GRA is more suitable for predicting software development effort with unbalanced dataset

Enhanced n-version programming and recovery block techniques for web service systems

In recent years, web services (WS’s) have been widely used to support interoperable machine-to-machine interaction over a network. In order to ensure a reliable WS system, a number of fault tolerance designs have been proposed. It is known that network connection and hardware devices may fail. In addition, the acceptance test (AT) as well as the decision mechanism (DM), which are common in fault tolerance designs, could also fail unexpectedly. Such uncertainties may affect the reliability of a WS-based system but have not yet been carefully considered in reliability modeling. Therefore, we propose extended NVP (ENVP) and extended RB (ERB) for the reliability analysis. Various operations of ENVP and ERB are discussed, and a simulation procedure is implemented to evaluate the system reliability and the failure probability of fault-tolerant WS-based systems. The experimental results show a high degree of correlation between the numbers of AT’s and the reliability improvements. The proposed fault tolerance designs could improve the system reliability, and the simulation procedure could also help in exploring appropriate configurations of fault tolerance designs for practitioners.

Evaluation and Analysis of Spectrum-Based Fault Localization with Modified Similarity Coefficients for Software Debugging

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.