Automated web GUI testing has been widely adopted since manual testing is time-consuming and tedious. Waiting strategy plays a vital role in automated web GUI testing since it significantly impacts the testing performance. Though important, little focus has been set on the waiting strategies in web GUI testing. Existing waiting strategies either wait for a predetermined time, which is not reliable in a dynamic environment, or only wait for a specific condition to be verified, which is often not robust enough to handle the complicated testing scenarios. In this work, we introduce a robust waiting strategy. Instead of waiting for a predetermined time or waiting for the availability of a particular element, our approach waits for a desired state to reach. This is achieved by capturing the Document Object Models (DOM) at the desired point, followed by an offline analysis to identify the differences between the DOMs associated with every two consecutive test actions. Such differences are used to determine the appropriate waiting time when automatically generating tests. Evaluation results with an industrial web application indicate that our approach produces more robust tests than the conventional waiting strategies used in web GUI testing. Furthermore, our generated tests are more representative of the recorded usage scenarios and are efficient with low overhead in test execution time.

Database-centric architectures have been widely adopted in large-scale software systems in various domains to deal with the ever-increasing amount and complexity of data. Prior studies have proposed a wide range of performance analytic techniques aimed at assisting developers in pinpointing software performance inefficiencies and diagnosing performance issues. However, directly applying these existing techniques to large-scale database-centric systems can be challenging and may not perform well due to the unique nature of such systems. In particular, compared to typical database-based systems like online shopping systems, in database-centric systems, a majority of the business logic and calculations reside in the database instead of the application. As the calculations in the database typically use domain-specific languages such as SQL, the performance issues of such systems and their diagnosis may be significantly different from the systems dominated by traditional programming languages such as Java. In this paper, we share our experience of adapting performance analytic techniques in a large-scale database-centric system from our industrial collaborator. Our adapted performance analysis pays special attention to the database and the interactions between the database and the application with minimal reliance on expert knowledge and manual effort. Moreover, we document our encountered challenges and how they are addressed during the development and adoption of our solution in the industrial setting as well as the corresponding lessons learned. We also discuss the real-world performance issues detected by applying our analysis to the target database-centric system. We anticipate that our solution and the reported experience can be helpful for practitioners and researchers who would like to ensure and improve the performance of database-centric systems.

Software developers usually rely on in-house performance testing to detect performance regressions and locate their root causes. Such performance testing is typically resource and time-consuming, making it impractical to conduct when the software is delivered in fast-paced release cycles. On the other hand, the operational data generated in the eld environment provides rich information about the performance of a software system and its runtime activities. Therefore, this work explores the idea of leveraging the readily-available eld operational data to locate the root causes of performance regression instead of running expensive performance tests. However, due to the ever-changing workloads from the end users and the noise from the eld, directly analyzing performance metrics such as response time of the system may not be able to help locate the root causes of performance regressions. In this paper, we report our experience of designing and adopting an approach that automatically locates the root causes of performance regressions while the software systems are deployed and running in the eld. First, our approach uses black-box performance models to capture the relationship between the performance of a system and its runtime activities. Then, our approach analyzes the performance models and uses statistical techniques to suggest the problematic system runtime activities (i.e., the root causes) that are related to a performance regression. Our evaluation considered three open-source projects and one industrial product. In the three open-source systems, we nd that our approach can successfully locate the root causes of all arbitrarily injected synthetic performance regressions. Our approach has successfully detected and located the root causes of three performance regressions in an industry system and it has been adopted by our industrial partner and used in practice on a daily basis over a 12-month period. In addition, we share the challenges that we encountered during the design and adoption of our approach, how we address those challenges, and the lessons that we learned during the process. We believe that our novel approach together with our documented experience can benet practitioners and researchers who wish to leverage the eld-operation data of a software system to conduct performance assurance activities.