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

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

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

COBOL systems migration to SOA: Assessing antipatterns and complexity

SOA and Web Services allow users to easily expose business functions to build larger distributed systems. However, legacy systems - mostly in COBOL - are left aside unless applying a migration approach. The main approaches are direct and indirect migration. The former implies wrapping COBOL programs with a thin layer of a Web Service oriented language/platform. The latter needs reengineering COBOL functions to a modern language/ platform. In our previous work, we presented an intermediate approach based on direct migration where developed Web Services are later refactored to improve the quality of their interfaces. Refactorings mainly capture good practices inherent to indirect migration. For this, antipatterns for WSDL documents (common bad practices) are detected to prevent issues related to WSDLs understanding and discoverability. In this paper, we assess antipatterns of Web Services’ WSDL documents generated upon the three migration approaches. In addition, generated Web Services’ interfaces are measured in complexity to attend both comprehension and interoperability. We apply a metric suite (by Baski & Misra) to measure complexity on services interfaces - i.e., WSDL documents. Migrations of two real COBOL systems upon the three approaches were assessed on antipatterns evidences and the complexity level of the generated SOA frontiers - a total of 431 WSDL documents.Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Flores, Andrés Pablo. Universidad Nacional del Comahue. Facultad de Informática. Departamento Ingeniería de Sistemas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Misra, Sanjay. Atilim University; Turquía. Covenant University; Nigeri

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

Intelligent Web Services Architecture Evolution Via An Automated Learning-Based Refactoring Framework

Architecture degradation can have fundamental impact on software quality and productivity, resulting in inability to support new features, increasing technical debt and leading to significant losses. While code-level refactoring is widely-studied and well supported by tools, architecture-level refactorings, such as repackaging to group related features into one component, or retrofitting files into patterns, remain to be expensive and risky. Serval domains, such as Web services, heavily depend on complex architectures to design and implement interface-level operations, provided by several companies such as FedEx, eBay, Google, Yahoo and PayPal, to the end-users. The objectives of this work are: (1) to advance our ability to support complex architecture refactoring by explicitly defining Web service anti-patterns at various levels of abstraction, (2) to enable complex refactorings by learning from user feedback and creating reusable/personalized refactoring strategies to augment intelligent designers’ interaction that will guide low-level refactoring automation with high-level abstractions, and (3) to enable intelligent architecture evolution by detecting, quantifying, prioritizing, fixing and predicting design technical debts. We proposed various approaches and tools based on intelligent computational search techniques for (a) predicting and detecting multi-level Web services antipatterns, (b) creating an interactive refactoring framework that integrates refactoring path recommendation, design-level human abstraction, and code-level refactoring automation with user feedback using interactive mutli-objective search, and (c) automatically learning reusable and personalized refactoring strategies for Web services by abstracting recurring refactoring patterns from Web service releases. Based on empirical validations performed on both large open source and industrial services from multiple providers (eBay, Amazon, FedEx and Yahoo), we found that the proposed approaches advance our understanding of the correlation and mutual impact between service antipatterns at different levels, revealing when, where and how architecture-level anti-patterns the quality of services. The interactive refactoring framework enables, based on several controlled experiments, human-based, domain-specific abstraction and high-level design to guide automated code-level atomic refactoring steps for services decompositions. The reusable refactoring strategy packages recurring refactoring activities into automatable units, improving refactoring path recommendation and further reducing time-consuming and error-prone human intervention.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/142810/1/Wang Final Dissertation.pdfDescription of Wang Final Dissertation.pdf : Dissertatio

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

Early Quality of Service Prediction via Interface-level Metrics, Code-level Metrics, and Antipatterns

https://deepblue.lib.umich.edu/bitstream/2027.42/155332/1/IST___Webservices (12).pd