To visualize the progress of the software development and to foster the awareness of software quality, visualizations of the software build chain have been established in recent years and can be applied in the software service domain easily. Other domains like mechanical engineering are lacking a comprehensive view of the software build chain, since existing of-the-shelf solutions do not support the integration of software quality assurance activities outside the responsibility of the used CI tool adequately. In this paper we present a prototype of a tool that supports the requirement of an extended build chain visualization. In close cooperation with an industrial partner company, we implemented a flexible, adaptable, web based visualization system called DevOpsView and we discuss our findings about the future of customized build chain visualization systems.

We propose Guided Random Testing (GRT), which uses static and dynamic analysis to include information on program types, data, and dependencies in various stages of automated test generation. Static analysis extracts knowledge from the system under test. Test coverage is further improved through state fuzzing and continuous coverage analysis. We evaluated GRT on 32 real-world projects and found that GRT outperforms major peer techniques in terms of code coverage (by 13 %) and mutation score (by 9 %). On the four studied benchmarks of Defects4J, which contain 224 real faults, GRT also shows better fault detection capability than peer techniques, finding 147 faults (66 %). Furthermore, in an in-depth evaluation on the latest versions of ten popular real-world projects, GRT successfully detects over 20 unknown defects that were confirmed by developers.

While being highly automated and easy to use, existing techniques of random testing suffer from low code coverage and defect detection ability for practical software applications. Most tools use a pure black-box approach, which does not use knowledge specific to the software under test. Mining and leveraging the information of the software under test can be promising to guide random testing to overcome such limitations. Guided Random Testing (GRT) implements this idea. GRT performs static analysis on software under test to extract relevant knowledge and further combines the information extracted at run-time to guide the whole test generation procedure. GRT is highly configurable, with each of its six program analysis components implemented as a pluggable module whose parameters can be adjusted. Besides generating test cases, GRT also automatically creates a test coverage report. We show our experience in GRT tool development and demonstrate its practical usage using two concrete application scenarios.