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 First Look at the Deprecation of RESTful APIs: An Empirical Study

REpresentational State Transfer (REST) is considered as one standard software architectural style to build web APIs that can integrate software systems over the internet. However, while connecting systems, RESTful APIs might also break the dependent applications that rely on their services when they introduce breaking changes, e.g., an older version of the API is no longer supported. To warn developers promptly and thus prevent critical impact on downstream applications, a deprecated-removed model should be followed, and deprecation-related information such as alternative approaches should also be listed. While API deprecation analysis as a theme is not new, most existing work focuses on non-web APIs, such as the ones provided by Java and Android. To investigate RESTful API deprecation, we propose a framework called RADA (RESTful API Deprecation Analyzer). RADA is capable of automatically identifying deprecated API elements and analyzing impacted operations from an OpenAPI specification, a machine-readable profile for describing RESTful web service. We apply RADA on 2,224 OpenAPI specifications of 1,368 RESTful APIs collected from APIs.guru, the largest directory of OpenAPI specifications. Based on the data mined by RADA, we perform an empirical study to investigate how the deprecated-removed protocol is followed in RESTful APIs and characterize practices in RESTful API deprecation. The results of our study reveal several severe deprecation-related problems in existing RESTful APIs. Our implementation of RADA and detailed empirical results are publicly available for future intelligent tools that could automatically identify and migrate usage of deprecated RESTful API operations in client code

Assessing Web Services Interfaces with Lightweight Semantic Basis

In the last years, Web Services have become the technological choice to materialize the Service-Oriented Computing paradigm. However, a broad use of Web Services requires efficient approaches to allow service consumption from within applications. Currently, developers are compelled to search for suitable services mainly by manually exploring Web catalogs, which usually show poorly relevant information, than to provide the adequate "glue-code" for their assembly. This implies a large effort into discovering, selecting and adapting services. To overcome these challenges, this paper presents a novel Web Service Selection Method. We have defined an Interface Compatibility procedure to assess structural-semantic aspects from functional specifications - in the form of WSDL documents - of candidate Web Services. Two different semantic basis have been used to define and implement the approach: WordNet, a widely known lexical dictionary of the English language; and DISCO, a database which indexes co-occurrences of terms in very large text collections. We performed a set of experiments to evaluate the approach regarding the underlying semantic basis and against third-party approaches with a data-set of real-life Web Services. Promising results have been obtained in terms of well-known metrics of the Information Retrieval field

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

Explainable, Security-Aware and Dependency-Aware Framework for Intelligent Software Refactoring

As software systems continue to grow in size and complexity, their maintenance continues to become more challenging and costly. Even for the most technologically sophisticated and competent organizations, building and maintaining high-performing software applications with high-quality-code is an extremely challenging and expensive endeavor. Software Refactoring is widely recognized as the key component for maintaining high-quality software by restructuring existing code and reducing technical debt. However, refactoring is difficult to achieve and often neglected due to several limitations in the existing refactoring techniques that reduce their effectiveness. These limitation include, but not limited to, detecting refactoring opportunities, recommending specific refactoring activities, and explaining the recommended changes. Existing techniques are mainly focused on the use of quality metrics such as coupling, cohesion, and the Quality Metrics for Object Oriented Design (QMOOD). However, there are many other factors identified in this work to assist and facilitate different maintenance activities for developers: 1. To structure the refactoring field and existing research results, this dissertation provides the most scalable and comprehensive systematic literature review analyzing the results of 3183 research papers on refactoring covering the last three decades. Based on this survey, we created a taxonomy to classify the existing research, identified research trends and highlighted gaps in the literature for further research. 2. To draw attention to what should be the current refactoring research focus from the developers’ perspective, we carried out the first large scale refactoring study on the most popular online Q&A forum for developers, Stack Overflow. We collected and analyzed posts to identify what developers ask about refactoring, the challenges that practitioners face when refactoring software systems, and what should be the current refactoring research focus from the developers’ perspective. 3. To improve the detection of refactoring opportunities in terms of quality and security in the context of mobile apps, we designed a framework that recommends the files to be refactored based on user reviews. We also considered the detection of refactoring opportunities in the context of web services. We proposed a machine learning-based approach that helps service providers and subscribers predict the quality of service with the least costs. Furthermore, to help developers make an accurate assessment of the quality of their software systems and decide if the code should be refactored, we propose a clustering-based approach to automatically identify the preferred benchmark to use for the quality assessment of a project. 4. Regarding the refactoring generation process, we proposed different techniques to enhance the change operators and seeding mechanism by using the history of applied refactorings and incorporating refactoring dependencies in order to improve the quality of the refactoring solutions. We also introduced the security aspect when generating refactoring recommendations, by investigating the possible impact of improving different quality attributes on a set of security metrics and finding the best trade-off between them. In another approach, we recommend refactorings to prioritize fixing quality issues in security-critical files, improve quality attributes and remove code smells. All the above contributions were validated at the large scale on thousands of open source and industry projects in collaboration with industry partners and the open source community. The contributions of this dissertation are integrated in a cloud-based refactoring framework which is currently used by practitioners.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/171082/1/Chaima Abid Final Dissertation.pdfDescription of Chaima Abid Final Dissertation.pdf : Dissertatio