DOMAIN: SOFTWARE ENGINEERING
| S. No. | IEEE TITLE | ABSTRACT | IEEE YEAR |
| 1. | Estimating Computational Requirements in Multi-Threaded Applications | Performance models provide effective support for managing quality-of-service (QoS) and costs of enterprise applications. However, expensive high-resolution monitoring would be needed to obtain key model parameters, such as the CPU consumption of individual requests, which are thus more commonly estimated from other measures. However, current estimators are often inaccurate in accounting for scheduling in multi-threaded application servers. To cope with this problem, we propose novel linear regression and maximum likelihood estimators. Our algorithms take as inputs response time and resource queue measurements and return estimates of CPU consumption for individual request types. Results on simulated and real application datasets indicate that our algorithms provide accurate estimates and can scale effectively with the threading levels. | 2015 |
| 2. | Instance Generator and Problem Representation to Improve Object Oriented Code Coverage | Search-based approaches have been extensively applied to solve the problem of software test-data generation. Yet, test-data generation for object-oriented programming (OOP) is challenging due to the features of OOP, e.g., abstraction, encapsulation, and visibility that prevent direct access to some parts of the source code. To address this problem we present a new automated search-based software test-data generation approach that achieves high code coverage for unit-class testing. We first describe how we structure the test-data generation problem for unit-class testing to generate relevant sequences of method calls. Through a static analysis, we consider only methods or constructors changing the state of the class-under-test or that may reach a test target. Then we introduce a generator of instances of classes that is based on a family of means-of-instantiation including subclasses and external factory methods. It also uses a seeding strategy and a diversification strategy to increase the likelihood to reach a test target. Using a search heuristic to reach all test targets at the same time, we implement our approach in a tool, JTExpert, that we evaluate on more than a hundred Java classes from different open-source libraries. JTExpert gives better results in terms of search time and code coverage than the state of the art, EvoSuite, which uses traditional techniques. | 2015 |
| 3. | STAR: Stack Trace Based Automatic Crash Reproduction via Symbolic Execution | Software crash reproduction is the necessary first step for debugging. Unfortunately, crash reproduction is often labor intensive. To automate crash reproduction, many techniques have been proposed including record-replay and post-failure-process approaches. Record-replay approaches can reliably replay recorded crashes, but they incur substantial performance overhead to program executions. Alternatively, post-failure-process approaches analyse crashes only after they have occurred. Therefore they do not incur performance overhead. However, existing post-failure-process approaches still cannot reproduce many crashes in practice because of scalability issues and the object creation challenge. This paper proposes an automatic crash reproduction framework using collected crash stack traces. The proposed approach combines an efficient backward symbolic execution and a novel method sequence composition approach to generate unit test cases that can reproduce the original crashes without incurring additional runtime overhead. Our evaluation study shows that our approach successfully exploited 31 (59.6 percent) of 52 crashes in three open source projects. Among these exploitable crashes, 22 (42.3 percent) are useful reproductions of the original crashes that reveal the crash triggering bugs. A comparison study also demonstrates that our approach can effectively outperform existing crash reproduction approaches | 2015 |
IEEE 2015 Dotnet Software Engineering Projects Title Abstract List Topics