Dimensionality Reduction of Quality Objectives for Web Services Design Modularization

With the increasing use of service-oriented Architecture (SOA) in new software development, there is a growing and urgent need to improve current practice in service-oriented design. To improve the design of Web services, the search for Web services interface modularization solutions deals, in general, with a large set of conflicting quality metrics. Deciding about which and how the quality metrics are used to evaluate generated solutions are always left to the designer. Some of these objectives could be correlated or conflicting. In this paper, we propose a dimensionality reduction approach based on Non-dominated Sorting Genetic Algorithm (NSGA-II) to address the Web services re-modularization problem. Our approach aims at finding the best-reduced set of objectives (e.g. quality metrics) that can generate near optimal Web services modularization solutions to fix quality issues in Web services interface. The algorithm starts with a large number of interface design quality metrics as objectives (e.g. coupling, cohesion, number of ports, number of port types, and number of antipatterns) that are reduced based on the nonlinear correlation information entropy (NCIE).The statistical analysis of our results, based on a set of 22 real world Web services provided by Amazon and Yahoo, confirms that our dimensionality reduction Web services interface modularization approach reduced significantly the number of objectives on several case studies to a minimum of 2 objectives and performed significantly better than the state-of-the-art modularization techniques in terms of generating well-designed Web services interface for users.Master of ScienceSoftware Engineering, College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145687/1/Thesis Report_Hussein Skaf.pdfDescription of Thesis Report_Hussein Skaf.pdf : Thesi

Detection of Web Service Refactoring Opportunities

We propose, in this thesis, to consider the problem of Web service antipatterns detection as a multi-objective problem where examples of Web service antipatterns and well-designed code are used to generate detection rules. To this end, we use multi-objective genetic programming (MOGP) to find the best combination of metrics that maximizes the detection of Web service antipattern examples and minimizes the detection of well-designed Web service design examples. We report the results of an empirical study using 8 different types of common Web service antipatterns. We compared our multi-objective formulation with random search, one existing mono-objective approach, and one state-of-the-art detection technique not based on heuristic search. Statistical analysis of the obtained results demonstrates that our approach is efficient in antipattern detection, on average, with a precision score of 94% and a recall score of 92%.Master of ScienceComputer and Information Science, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/136611/1/Thesis Report_Taghreed Hassouna22MARCH.pdfDescription of Thesis Report_Taghreed Hassouna22MARCH.pdf : Thesi

Prediction of Web Service Antipatterns Using Machine Learning

Web service interfaces are considered as one of the critical components of a Service-Oriented Architecture (SOA) and they represent contracts between web service providers and clients (subscribers). These interfaces are frequently modified to meet new requirements. However, these changes in a web service interface typically affect the systems of its subscribers. Thus, it is important for subscribers to estimate the risk of using a specific service and to compare its evolution to other services offering the same features in order to reduce the effort of adapting their applications in the next releases. In addition, the prediction of interface changes may help web service providers to better manage available resources (e.g. programmers’ availability, hard deadlines, etc.) and efficiently schedule required maintenance activities to improve the quality. In this research, we propose to use machine learning, based on times series, for the prediction of web service antipatterns. To this end, we collected training data from quality metrics of previous releases from 8 web services. The validation of our prediction techniques shows that the predicted metrics value, such as number of operations, which are used to feed the antipattern detection rules on the different releases of the 8 web services were similar to the expected ones with a very low deviation rate. In addition, most of the quality issues of the studied Web service interfaces were accurately predicted, for the next releases. The survey conducted with active developers also shows the relevance of prediction technique for both service providers and subscribers.Master of ScienceSoftware Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/136193/1/PredictionOfWebServiceAntipatternsUsingMachineLearning (1).pdfDescription of PredictionOfWebServiceAntipatternsUsingMachineLearning (1).pdf : Thesi

A model-driven approach to broaden the detection of software performance antipatterns at runtime

Performance antipatterns document bad design patterns that have negative influence on system performance. In our previous work we formalized such antipatterns as logical predicates that predicate on four views: (i) the static view that captures the software elements (e.g. classes, components) and the static relationships among them; (ii) the dynamic view that represents the interaction (e.g. messages) that occurs between the software entities elements to provide the system functionalities; (iii) the deployment view that describes the hardware elements (e.g. processing nodes) and the mapping of the software entities onto the hardware platform; (iv) the performance view that collects specific performance indices. In this paper we present a lightweight infrastructure that is able to detect performance antipatterns at runtime through monitoring. The proposed approach precalculates such predicates and identifies antipatterns whose static, dynamic and deployment sub-predicates are validated by the current system configuration and brings at runtime the verification of performance sub-predicates. The proposed infrastructure leverages model-driven techniques to generate probes for monitoring the performance sub-predicates and detecting antipatterns at runtime.Comment: In Proceedings FESCA 2014, arXiv:1404.043

A Bi-Level Multi-Objective Approach for Web Service Design Defects Detection

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152453/1/JSS_WSBi_Level__Copy_fv.pd

applying design patterns to remove software performance antipatterns a preliminary approach

Abstract: Patterns and antipatterns represent powerful instruments in the hands of software designers, for improving the quality of software systems. A large variety of design patterns arose since decades, as well as several performance antipatterns have been defined. In this paper we propose a preliminary approach for antipattern-based refactoring of software systems, driven by design patterns application. The approach is focused on refactoring software artifacts (i.e., models, code) by applying design patterns, with the aim of removing possible performance antipatterns occurring on such artifacts. Based on our approach, design patterns are ranked in order to drive the refactoring choice. We also provide an illustrative example as a preliminary validation of our approach, showing how the ranking method works over three design patterns for removing the Empty Semi-Trucks performance antipattern, and we finally identify future research directions of our work

Exploring Maintainability Assurance Research for Service- and Microservice-Based Systems: Directions and Differences

To ensure sustainable software maintenance and evolution, a diverse set of activities and concepts like metrics, change impact analysis, or antipattern detection can be used. Special maintainability assurance techniques have been proposed for service- and microservice-based systems, but it is difficult to get a comprehensive overview of this publication landscape. We therefore conducted a systematic literature review (SLR) to collect and categorize maintainability assurance approaches for service-oriented architecture (SOA) and microservices. Our search strategy led to the selection of 223 primary studies from 2007 to 2018 which we categorized with a threefold taxonomy: a) architectural (SOA, microservices, both), b) methodical (method or contribution of the study), and c) thematic (maintainability assurance subfield). We discuss the distribution among these categories and present different research directions as well as exemplary studies per thematic category. The primary finding of our SLR is that, while very few approaches have been suggested for microservices so far (24 of 223, ?11%), we identified several thematic categories where existing SOA techniques could be adapted for the maintainability assurance of microservices