Regression testing is widely studied in the literature, although most research on the topic is concerned with improving specific sub-challenges of a wider goal. Test suite orchestration proposes a more comprehensive view of the challenge of regression testing, by merging and combining different techniques with a variety of objectives, including prioritizing, selecting, reducing and amplifying tests, detecting flaky tests and potentially more. This paper presents the key approaches and techniques that form test suite orchestration, along with common evaluation metrics, and discusses how they can be used together to ultimately provide an efficient and effective regression testing strategy. To illustrate the benefits of orchestration, we provide some examples of existing papers that take steps towards this goal, even if the specific terminology is not yet used. Orchestrated strategies utilizing existing regression testing techniques provide a pathway to practicality and real-world usage of the academic literature.

Test case selection (TCS) and test case prioritization (TCP) techniques can reduce time to detect the first test failure. Although these techniques have been extensively studied in combination and isolation, they have not been compared one against the other. In this paper, we perform an empirical study directly comparing TCS and TCP approaches, represented by the tools Ekstazi and FAST, respectively. Furthermore, we develop the first combination, named Fastazi, of file-based TCS and similarity-based TCP and evaluate its benefit and cost against each individual technique. We performed our experiments using 12 Java-based open-source projects. Our results show that, in the median case, the combined approach detects the first failure nearly two times faster than either Ekstazi alone (with random test ordering) or FAST alone (without TCS). Statistical analysis shows that the effectiveness of Fastazi is higher than that of Ekstazi, which in turn is higher than that of FAST. On the other hand, FAST adds the least overhead to testing time, while the difference between the additional time needed by Ekstazi and Fastazi is negligible. Fastazi can also improve failure detection in scenarios where the time available for testing is restricted.

By integrating processors and wireless communication units into vehicles, it is possible to create a vehicular ad-hoc network (VANET), in which cars share data amongst themselves in order to cooperate and make roads safer and more efficient. A decentralized ad-hoc solution, which does not rely on previously existing infrastructure, Internet connection or server availability, is preferred so the message delivery latency is as short as possible in the case of life-critical situations. However, as it is the case with most new technologies, VANETs will be a prime target for attacks performed by malicious users, who may benefit from affecting traffic conditions. In order to avoid such attacks, one important feature for vehicular networks is trust management, which allows nodes to filter incoming messages according to previously established trust values assigned to other nodes. To generate these trust values, nodes use information acquired from past interactions. Nodes which frequently share false or irrelevant data must have lower trust values than the ones which appear to be reliable. This work proposes TruMan, a trust management model in the context of daily commutes, utilizing the Working Day Movement Model as a basis for node mobility. The results prove to be accurate, detecting nearly all malicious nodes with very few false positives when they constitute up to 50% of the network. The model is also very efficient thanks to the low complexity of the algorithm constituting the trust model.