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In software product line engineering (SPLE), many studies have been conducted on commonality- and variability-based feature extraction methods and on the reasoning and refinement of feature models (FMs), aiming to enhance the appropriateness and reusability of the constructed FMs in compliance with feature-oriented development. The existing methods, however, failed to assure the developed applications that contain ambiguities between the features generated in FMs by analyzers' intuitions, and hindered the reuse of such applications. Moreover, the accuracy measurements of models based on mathematics-based theoretical verification methods are difficult to apply in practice. Therefore, a refinement technique is demanded to enhance the FM accuracy.

This paper aims to identify abnormal feature duplications and collisions based on the feature attributes to address the potential ambiguities between the features in an FM generated for a target domain, and to construct more precise FMs by presenting a technique for eliminating such abnormalities. For this purpose, the profiles of the formalized attributes were first defined based on MDR. Based on the semantics and relationships between the attributes, the duplications and collisions were identified using an analysis matrix, and were generalized to formulate rules by level. Such rules were evaluated to remove the duplications and collisions. In addition, using a supporting analyzer, the features in the initial FM were registered on a repository and were analyzed for feature duplications and collisions based on the saved attribute data.

The refinements of the ambiguities between such features are likely to enable the construction of more precise application FMs and the generation of common features with higher reusability. Further, the environments using support tools are expected to provide convenience in the similarity analysis and reuse of features.

Testing activities for software product lines should be different from that of single software systems, due to significant differences between software product line engineering and single software system development. The cost of testing in software product line is generally higher compared with single software systems; therefore, there should exist a certain balance between cost, quality of final products, and the time of performing testing activities. As decreasing testing cost is an important challenge in software product line integration testing, the contribution of this paper is in introducing a method for early integration testing in software product lines based on feature model (FM) by prioritizing test cases in order to decrease integration testing costs in SPLs. In this method, we focus on reusing domain engineering artifacts and prioritized selection and execution of integration test cases. It also uses separation of concerns and pruning techniques on FMs to help prioritize the test cases. The method shows to be promising when applied to some case studies in the sense that it decreases the costs of performing integration test by about 82% and also detects about 44% of integration faults in domain engineering.

The Feature Model (FM) is a fundamental artifact of the Software Product Line (SPL) engineering, used to represent commonalities and variabilities, and also to derive products for testing. However, the test of all features combinations (products) is not always possible in practice. Due to the growing complexity of the applications, only a subset of products is usually selected. The selection is generally based on combinatorial testing, to test features interactions. This kind of selection does not consider different classes of faults that can be present in the FM. The application of a fault-based approach, such as mutation-based testing, can increase the probability of finding faults and the confidence that the SPL products match the requirements. Considering that, this paper introduces a mutation approach to select products for the feature testing of SPLs. The approach can be used similarly to a test criterion in the generation and assessment of test cases. It includes (i) a set of mutation operators, introduced to describe typical faults associated to the feature management and to the FM; and (ii) a testing process to apply the operators. Experimental results show the applicability of the approach. The selected test case sets are capable to reveal other kind of faults, not revealed in the pairwise testing.

Software product line engineering (SPLE) is a paradigm to promote systematic software reuse. A Feature Model (FM) is a common means to illustrate the commonality and variability of software products in a family. In most existing FM extraction approaches, keywords in the requirement document or certain types of system behavior or external events are considered features. The resulting FM is a combination of user activities and system actions (SAs), making it hard to understand. In this paper, we present an automatic approach to generate a product line FM from multiple requirement documents. We consider user activity and SAs separately in our approach and focus on the expected behaviors of the software system, together with the data being processed. The resulting FM clearly illustrates the expected functionalities of the software system and their variability in the product line. We also compared our approach with existing techniques by processing the same textual documents, and noted improvements in our results.

Temporal relation exits between concepts of time and event. It provides a natural clue for information organization. This paper studies the method of temporal expression recognition on the basis of information extraction and temporal denotation. We analyze the features of temporal expressions and conclude that the temporal expressions are constructed by temporal expression baseline and temporal expression director, which respectively indicate time reference and time point or time range and are the theory reference of recognition rules. Rule based method is used to extract explicit time information because of the certainty feature of it. Machine learning based method is used to recognize the implicit time information because there are no definite rules in it. The experiment shows the blended method is of relatively high accuracy and has higher recall rate than an individual method.