Biblio

The purpose of this paper is to analyze all Cloud based Service Models, Continuous Integration, Deployment and Delivery process and propose an Automated Continuous Testing and testing as a service based TestBot and metrics dashboard which will be integrated with all existing automation, bug logging, build management, configuration and test management tools. Recently cloud is being used by organizations to save time, money and efforts required to setup and maintain infrastructure and platform. Continuous Integration and Delivery is in practice nowadays within Agile methodology to give capability of multiple software releases on daily basis and ensuring all the development, test and Production environments could be synched up quickly. In such an agile environment there is need to ramp up testing tools and processes so that overall regression testing including functional, performance and security testing could be done along with build deployments at real time. To support this phenomenon, we researched on Continuous Testing and worked with industry professionals who are involved in architecting, developing and testing the software products. A lot of research has been done towards automating software testing so that testing of software product could be done quickly and overall testing process could be optimized. As part of this paper we have proposed ACT TestBot tool, metrics dashboard and coined 4S quality metrics term to quantify quality of the software product. ACT testbot and metrics dashboard will be integrated with Continuous Integration tools, Bug reporting tools, test management tools and Data Analytics tools to trigger automation scripts, continuously analyze application logs, open defects automatically and generate metrics reports. Defect pattern report will be created to support root cause analysis and to take preventive action.

The use of risk information can help software engineers identify software components that are likely vulnerable or require extra attention when testing. Some studies have shown that the requirements risk-based approaches can be effective in improving the effectiveness of regression testing techniques. However, the risk estimation processes used in such approaches can be subjective, time-consuming, and costly. In this research, we introduce a fuzzy expert system that emulates human thinking to address the subjectivity related issues in the risk estimation process in a systematic and an efficient way and thus further improve the effectiveness of test case prioritization. Further, the required data for our approach was gathered by employing a semi-automated process that made the risk estimation process less subjective. The empirical results indicate that the new prioritization approach can improve the rate of fault detection over several existing test case prioritization techniques, while reducing threats to subjective risk estimation.

Automated tests play an important role in software evolution because they can rapidly detect faults introduced during changes. In practice, code-coverage metrics are often used as criteria to evaluate the effectiveness of test suites with focus on regression faults. However, code coverage only expresses which portion of a system has been executed by tests, but not how effective the tests actually are in detecting regression faults. Our goal was to evaluate the validity of code coverage as a measure for test effectiveness. To do so, we conducted an empirical study in which we applied an extreme mutation testing approach to analyze the tests of open-source projects written in Java. We assessed the ratio of pseudo-tested methods (those tested in a way such that faults would not be detected) to all covered methods and judged their impact on the software project. The results show that the ratio of pseudo-tested methods is acceptable for unit tests but not for system tests (that execute large portions of the whole system). Therefore, we conclude that the coverage metric is only a valid effectiveness indicator for unit tests.

Concurrent programs are prone to various classes of difficult-to- detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SIMRT, an automated regression testing framework for use in de- tecting races introduced by code modifications. SIMRT employs a regression test selection technique, focused on sets of program ele- ments related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritiza- tion technique to improve the rate at which such races are detected. Our empirical study of SIMRT reveals that it is more efficient and effective for revealing races than other approaches, and that its con- stituent test selection and prioritization components each contribute to its performance.