International Journal of Software Engineering and Knowledge Engineering

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.

Software architects make architectural decisions such as choosing architecture patterns or frameworks. Capturing architectural decisions reduces evolution costs for software systems. Existing approaches overlook the challenge of capturing tacit knowledge about architectural decisions. Previously, we proposed the REGAIN approach to capture tacit knowledge about architectural decisions. REGAIN is based on the Repertory Grid technique, a powerful knowledge acquisition approach from knowledge engineering. However, REGAIN lacked industrial validation. Therefore, in this paper, we initially present a study to ensure that REGAIN meets the needs of industrial architects. We interviewed 16 architects who indicated REGAIN advantages such as systematic decision-making support. Also, architects indicated improvement opportunities, in particular tool support and the possibility to prioritize concerns that are used to evaluate decision alternatives. Therefore, we conducted an additional study to evaluate two approaches for prioritizing concerns: pairwise-comparisons and the hundred-dollar approach. We conducted an experiment with 30 graduate students to compare the two prioritization approaches. Based on the results of the experiment, we added the hundred-dollar approach to REGAIN. Moreover, based on the study with architects and the experiment with students, we implemented open source tool support for REGAIN, which architects can use to capture architectural decisions.

Achieving the effectiveness in relation to the relevance of query result is the most crucial part of XML keyword search. Developing an XML Keyword search approach which addresses the user search intention, keyword ambiguity problems and query/search result grading (ranking) problem is still challenging. In this paper, we propose a novel approach called XDMA for keyword search in XML databases that builds two indices to resolve these problems. Then, a keyword search technique based on two-level matching between two indices is presented. Further, by utilizing the logarithmic and probability functions, a terminology that defines the Mutual Score to find the desired T-typed node is put forward. We also introduce the similarity measure to retrieve the exact data through the selected T-typed node. In addition, grading for the query results having comparable relevance scores is employed. Finally, we demonstrate the effectiveness of our proposed approach, XDMA with a comprehensive experimental evaluation using the datasets of DBLP, WSU and eBay.

Many complex systems, such as software systems, are full of complexity arising from interactions among basic units (such as classes, interfaces and struts in object-oriented software systems). One of the most successful approaches to capture the underlying structural features of large-scale software systems is the investigation of hierarchical organization. However, the hierarchy of software networks has not been thoroughly investigated. In this paper, the crucial fraction (CF) in software networks has been extracted and analyzed in a set of real-world software systems. First, the classes and the relationships between them have been extracted into software networks. Then software networks have been divided into different layers, and CF of software networks has been extracted by k-core. The empirical studies in this paper reveal that software networks represent flat hierarchical structure. Finally, CF has been measured by the relevant complex network parameters respectively, and the relations between CF and overall network have been analyzed by the case studies of software networks. The results show that CF represents characteristics of scale-free, small-world, strong connectivity, and the units in CF are frequently reused and dominate the overall system.

A DataSpace Support Platform (DSSP) is a self-sustained and self-managed system which needs to support uncertainty among its mediated schemas and its schema mappings. Some approaches for managing such uncertainty by assigning probabilities and reliability degrees to schema mappings have been proposed. Unfortunately, the number of mappings self-generated by a DSSP is usually too large and among those possible mappings, some might be totally correct and others partially correct. Therefore, providing probabilities or reliability degrees to the mappings is necessary but not sufficient to resolve uncertainty among them. This paper proposes a stepper-based approach called pos-mapping to managing reliable mappings using possibility theory. Instead of choosing a threshold for managing the reliable mappings, pos-mapping approach orders and divides the set of reliable mappings into subsets of possibility distributions and assigns to each of these subsets a recursive possibility degree function. The recursiveness of the possibility degree function leads to an incremental management of the possibility distributions. Experimental results show that our system is more efficient than the existing systems and the accuracy of the results increases with the number of reliable schemas in the DSSP.

Time and resource constraints should be taken into account in software testing activities, and thus optimizing the test suite is fundamental in the development process. In this context, the test case selection aims to eliminate redundant or unnecessary test data, which is crucial for the definition of test strategies. This paper presents a systematic review on the test case selection conducted through a selection of 449 articles published in leading journals and conferences in Computer Science. We addressed the state-of-art by collecting and comparing existing evidence on the methods used in the different software domains and the methods used to evaluate the test case selection. Our study identified 32 papers that met the research objectives, which featured 18 different selection methods and were evaluated through 71 case studies. The most commonly reported methods are adaptive random testing, genetic algorithms and greedy algorithm. Most approaches rely on heuristics, such as diversity of test cases and code or model coverage. This paper also discusses the key concepts and approaches, areas of application and evaluation metrics inherent to the methods of test case selection available in the literature.

Software traceability is the ability to provide trace information on requirements, design, and implementation of a system. It helps stakeholders understand the many associations of software artifacts created during a software development project. End-to-end traceability refers to linkage of all artifacts in the entire lifecycle of a software development project. Its goal is to provide stakeholders of the software development with trace information in order to analyze impacts due to changes in a software system. Compared to that of a single product, the end-to-end traceability of software product line is more complicated because Software Product Line Development (SPLD) requires two separate but intimately related phases of domain engineering and application engineering. Various SPLD traceability approaches have been proposed in the past. However, thus far no research work on SPLD traceability has focused on SPLD end-to-end traceability. This paper defines SPLD end-to-end traceability and evaluates the existing SPLD traceability approaches from SPLD end-to-end traceability perspectives. We surveyed studies on SPLD traceability methods, traceability mechanisms used in major SPLD approaches, and software traceability survey papers. We compared the existing SPLD traceability approaches based on Systematic Literature Review (SLR). Through the survey, we found that none of the SPLD traceability studies fully supports SPLD end-to-end traceability, and there are unexplored research areas of SPLD end-to-end traceability in the existing SPLD traceability studies. The contribution of this paper is that it presents future research directions that give research guidelines for each unexplored research area in SPLD end-to-end traceability. Finally, based on the research directions, this paper suggests future research opportunities for SPLD end-to-end traceability.