International Journal of Software Engineering and Knowledge Engineering

Model transformation is a keystone in carrying out model-driven development. Currently, many studies have focused on model transformation, those researches study the automatic transformation from PIM to PSM and from PSM to code, but only few have dealt with how the CIM-level model is automatically mapped onto the PIM-level model. Even though some studies have proposed the semi-automatic transformation from CIM-level model to PIM-level model, only few mechanisms are discussed to protect the consistency between the CIM-level model and the PIM-level model in these researches because notation specification of these CIM-level model and PIM-level model does not contain precise semantics. Focusing on workflow perspective, this paper proposes an automatic model transformation approach, which applies a formal model to define the automatic mapping of the workflow from CIM-level model to PIM-level model. This proposal extends Petri nets model elements to define a bridge model, then we use this bridge model to link the CIM-level model (BPMN) and PIM-level model (SCM) following model-driven approach. The metamodels, the transformation rule and the transformation plug-ins have been defined in Eclipse Modeling Framework. Meanwhile, the plug-ins have been empirically validated by a Travel Agency case study. This study shows that using extended Petri nets model as a bridge model to define an automatic model transformation between CIM-level model and PIM-level model is feasible. This approach by means of formal model can ensure the correctness and completeness of the target model, and it can also maintain the consistency between the source model and target model.

Software defect prediction technology has been widely used in improving the quality of software system. Most real software defect datasets tend to have fewer defective modules than defective-free modules. Highly class-imbalanced data typically make accurate predictions difficult. The imbalanced nature of software defect datasets makes the prediction model classifying a defective module as a defective-free one easily. As there exists the similarity during the different software modules, one module can be represented by the sparse representation coefficients over the pre-defined dictionary which consists of historical software defect datasets. In this study, we make use of dictionary learning method to predict software defect. We optimize the classifier parameters and the dictionary atoms iteratively, to ensure that the extracted features (sparse representation) are optimal for the trained classifier. We prove the optimal condition of the elastic net which is used to solve the sparse coding coefficients and the regularity of the elastic net solution. Due to the reason that the misclassification of defective modules generally incurs much higher cost risk than the misclassification of defective-free ones, we take the different misclassification costs into account, increasing the punishment on misclassification defective modules in the procedure of dictionary learning, making the classification inclining to classify a module as a defective one. Thus, we propose a cost-sensitive software defect prediction method using dictionary learning (CSDL). Experimental results on the 10 class-imbalance datasets of NASA show that our method is more effective than several typical state-of-the-art defect prediction methods.

Data logging is helpful for the operation and maintenance manager of SaaS-based solutions to diagnose performance issues. However, long-running SaaS software may generate huge amounts of log data which is difficult to analyze, and it lacks a systematic approach to collect the running log and lacks a unified data structure to normalize the performance-related data. All these threaten the timeliness of SaaS performance issue diagnosis. In this paper, we propose an architecture for log collection and analysis to support the assessment of performance and diagnosis of performance issues of SaaS-based application in cloud computing. The architecture has the three-tier structure and includes a pivot data model to integrate heterogeneous log. The two high-level metrics in the model of Average Response Time (ART) and Request Timeout Rate (RTR) are calculated by statistical measurement and the lower-level metrics are monitored in real-time. Operation and maintenance managers can evaluate the performance of SaaS software based on the high-level metrics, then timely locate the issues from the low-level metrics and take appropriate measures. Thereupon, this study presents the general-purpose technique for the architecture to support real-time big log data collection, access, computation, storage. The proposal has been implemented and validated in a case study.

This paper improves the breadth-first search strategy in directed automated random testing (DART) to generate a fewer number of test data while gaining higher branch coverage, namely Static DART or SDART for short. In addition, the paper extends the test data compilation mechanism in DART, which currently only supports the projects written in C, to generate test data for C++ projects. The main idea of SDART is when it is less likely to increase code coverage with the current path selection strategies, the static test data generation will be applied with the expectation that more branches are covered earlier. Furthermore, in order to extend the test data compilation of DART for C++ context, the paper suggests a general test driver technique for C++ which supports various types of parameters including basic types, arrays, pointers, and derived types. Currently, an experimental tool has been implemented based on the proposal in order to demonstrate its efficacy in practice. The results have shown that SDART achieves higher branch coverage with a fewer number of test data in comparison with that of DART in practice.

This paper proposes a transformation-based method to automatically generate functional test cases from use cases named USLTG (Use case Specification Language (USL)-based Test Generation). We first focus on developing a modeling language named Test Case Specification Language (TCSL) in order to express test cases. Test cases in TCSL can contain detailed information including test steps, test objects within steps, actions of test objects, and test data. Such information is often ignored in currently available test case specifications. We then aim to generate test cases in a TCSL model by a transformation from use cases that are represented by a USL. The USLTG transformation includes three main steps in generating (1) scenarios, (2) test data, and (3) a TCSL model. Within our transformation, the OCL solver is employed in order to build system snapshots as the part of test cases and to identify other test data. We applied our method to two case studies and evaluated our method by comparing it with other recent works.

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy, an enhanced mutation strategy with local searching and an elite inheritance mechanism. Empirical results on the same case studies demonstrate that our algorithm significantly outperforms the state-of-the-art for four out of five SPLs on a quality Hypervolume indicator and the convergence speed. To verify the effectiveness and robustness of our algorithm, the parameter sensitivity analysis is discussed and three observations are reported in detail.