Sheng Huang

Regression Testing as a Service

Selective regression testing involves retesting of software systems with a subset of the test suites to verify that modifications have not adversely impacted existing functions. Although this problem has been heavily researched, it has never been discussed in the context of SaaS (Software as a service). This paper presents the specific requirements, challenges and benefits in delivering regression test selection as a service (RTaaS). We will introduce how to design and implement a RTaaS platform. An implementation of RTaaS has been piloted and improved via several real projects in China market. The real customer cases illustrate that RTaaS is a cost-effective and easy way for software project teams to leap over technical barriers and tap into advanced regression testing selection technologies.

Incremental sensor placement optimization on water network

Sensor placement on water networks is critical for the detection of accidental or intentional contamination event. With the development and expansion of cities, the public water distribution systems in cities are continuously growing. As a result, the current sensor placement will lose its effectiveness in detecting contamination event. Hence, in many real applications, we need to solve the incremental sensor placement (ISP) problem. We expect to find a sensor placement solution that reuses existing sensor deployments as much as possible to reduce cost, while ensuring the effectiveness of contamination detection. In this paper, we propose scenario-cover model to formalize ISP and prove that ISP is NP-hard and propose our greedy approaches with provable quality bound. Extensive experiments show the effectiveness, robustness and efficiency of the proposed solutions. n nSupported by the National NSFC (No. 61003001, 61170006, 61171132, 61033010); Specialized Research Fund for the Doctoral Program of Higher Education No. 20100071120032; NSF of Jiangsu Province (No. BK2010280).

A novel approach to regression test selection for J2EE applications

Selective regression testing involves retesting of software systems with a subset of the test suite to verify that modifications have not adversely impacted existing functions. The J2EE platform has come to dominate the commercial Java application market. Unlike standalone Java applications, J2EE applications also use configuration files to control the presentation, service, data access, and persistence layers. Traditional regression test selection approaches for standalone Java applications generate regression test suites by only deriving test cases that traverse the changed parts of pure Java classes and thus are generally insufficient for the regression testing scenarios of J2EE applications. This paper proposes an end-to-end regression test selection solution for J2EE applications by providing two unique features—hybrid test-case tracing and unified change identification—that are not addressed by existing approaches. An empirical study is presented to show that this approach can ensure change coverage, and reduce regression test cost for J2EE applications efficiently and effectively.

EAaaS: Edge Analytics as a Service

In the Internet of Things (IoT) era, with ubiquitous remote sensing devices and other diverse data sources, nearly everything can forward voluminous data continuously, in real-time, which drives demand to perform real-time analytics on uninterrupted IoT data flows. The typical resultant approach is a cloud-centered architecture providing an analytic service for real-time IoT data processing. However, a cloud-centered IoT analytic service cannot guarantee real-time responsiveness has a high-fee pay-as-you-go business model, and opens data privacy concerns. Hence, it becomes rational to shift analytic workloads to the edge and provide a management service for edge analysis. Existing work on providing edge analytics as a service encountered challenges such as lacking a lightweight way to compose IoT applications based on multiple service providers, lacking a flexible and unified way to define domain-specific analytic logic, and maintaining efficiency when processing data on a resource-limited edge. This paper presents EAaaS, a scalable analytic service for enabling real-time edge analytics in IoT scenarios. In this work, we propose a unified rule-based analytic model to ease users programming efforts in specifying rule-based analytic logic. Moreover, we also designed and implemented a high performance edge engine to apply rule-based analytic on incoming device data streams. To simplify the access to EAaaS service, a group of RESTful web interfaces is also designed for edge analytic management on cloud and flexible composition with external services. EAaaS is implemented as a part of IBM Watson IoT Platform, which is a cloud service for elementary IoT application development on IBM Bluemix cloud announced by IBM recently. We have conducted proof of correctness (PoC) of EAaaS with customers from boat racing in the U.S. and collected valuable feedback from customers for further enhancement of EAaaS’s flexibility and usability

XMLValue: XML Configuration Attribute Value Recommendation

Frameworks are popularly used to reduce implementation complexity and improve productivity. Unfortunately, most frameworks are quite complex and not well documented. Hence, correctly and effectively programming with Framework is still a great challenge. One of the significant obstacles for us to smoothly use Framework is the complicated attribute value configuration of XML files. To overcome these difficulties, we present XMLValue to automatically recommend XML attribute values using association rules and NLP techniques. Experimental results show that our tool is efficient and effective for mining reusable configuration snippets, and has significantly shorten development time for framework based programming, and is general enough to support a variety of frameworks, and has real time performance for code assistance.

Which topic will you follow

Who are the most appropriate candidates to receive a call-for-paper or call-for-participation? What session topics should we propose for a conference of next year? To answer these questions, we need to precisely predict research topics of authors. In this paper, we build a MLR (Multiple Logistic Regression) model to predict the topic-following behavior of an author. By empirical studies, we find that social influence and homophily are two fundamental driving forces of topic diffusion in SCN (Scientific Collaboration Network). Hence, we build the model upon the explanatory variables representing above two driving forces. Extensive experimental results show that our model can consistently achieves good predicting performance. Such results are independent of the tested topics and significantly better than that of state-of-the-art competitor.

XMLSnippet: A Coding Assistant for XML Configuration Snippet Recommendation

Framework-based1 applications are quite popularly used in current commercial applications. Framework-based applications are often controlled by XML configuration files. However, most of these frameworks are complex or not well documented, which poses a great challenge for programmers to correctly utilize them. To overcome these difficulties, we propose a new method to recommend XML configuration snippets automatically through mining tree patterns from the application repository with the aim of assisting the programmer to generate proper XML configurations during the product phase. This method is further realized into a new tool XMLSnippet. In this paper, we will systematically present the core techniques of the proposed method, including closed frequent tree pattern mining, prefix tree based indexing and three types of queries (type query, structural query and context query) for online coding assistance. Experimental results show that these techniques are efficient for recommending reusable XML configuration snippets and consequently improve the productivity of programmers effectively with the assistance of the tool when they program with XML-based frameworks.

Cloud Based Test Coverage Service

Test coverage is popularly applied in software companies to measure test efforts and create regression test suite. Many service providers actively provide test as a service to software companies to reduce the cost of software test. However, it is a big challenge to store increasing massive test coverage data, such as software build, test traces, etc. In addition, it is also a data-intensive processing for coverage statistics summarization and regression test generation. Hence, data availability and scalability becomes two obstacles for the traditional solutions to be deployed as a service. To overcome these obstacles, in this paper, we propose to leverage the cloud techniques including BigTable and MapReduce to deploy test coverage service.

Mining application repository to recommend XML configuration snippets

Framework-based applications controlled by XML configuration files are quite popularly used in current commercial applications. However, most of these frameworks are complex or not well documented, which poses a great challenge for programmers to correctly utilize them. To overcome these difficulties, we propose a new tool to recommend XML configuration snippets automatically through mining tree patterns and pattern associations from the application repository with the aim of assisting the programmer to generate proper XML configurations during the production phase. In this demo, we showcase this tool by presenting the major techniques behind the tool and the typical usage scenarios of our tool.

SCuV: a novel software clustering and visualization tool

Decomposing a software system into smaller, more manageable clusters provides an insight for better comprehension of large systems for software engineers. However, invocation-awareness and dynamic view are two features which are not supported by existed software clustering visualization tools. In this paper, we presents a novel tool, named SCuV, to partition the Software into invocation-aware clusters, Cluster them with nested containment & invocation hierarchy and Visualize the clustering result in granularity-adjustable way.