Adaptive retrieval is a technique to overcome the recall limitations of two-stage retrieval pipelines. Adaptive retrieval focuses mainly on effectiveness, but shows potential to improve efficiency. This research focuses on the trade-off between effectiveness and efficiency in adaptive retrieval. We explore the behaviour of neighbourhoods formed by the corpus graph, find that the effectiveness of adaptive retrieval varies across queries, and identify the most effective way to leverage the corpus graph. We investigate the impact of two different scoring mechanisms on the efficiency and effectiveness of an adaptive retrieval pipeline. We observe that score interpolation tends to improve adaptive retrieval's effectiveness, and incorporating an additional cross-encoder stage can lead to further gains. We compare our proposed solutions against several baselines to examine the trade-offs between effectiveness and efficiency. In our setting, we find that adaptive retrieval can improve efficiency at the cost of effectiveness. For our experiments, we introduce a visualisation tool for graph exploration and an adaptive retrieval component for efficient retrieval pipelines.

Big Data is an expanding industry, yet exhaustive and automated testing of Big Data applications is still in its early stages. In the last few years, testing framework for Big Data applications have started appearing. BigFuzz is a program that uses fuzz testing for Big Data applications. Fuzz testing means generating random, potentially invalid or erroneous, inputs in attempt to find exceptions. This paper introduces TabFuzz, a tool that improves and extends the BigFuzz solution. TabFuzz reproduces the BigFuzz implementation and extends on it, by improving the generation of random input files. TabFuzz can generate a valid input file based on an input specification. It then mutates this file using high-level mutations. These mutations generate new test inputs that mimic real-world problems. This is an improvement over bit or byte level mutations. These mutations are supposed to mimic real-world problem, which is an improvement over random bit or byte level mutations. Most fuzzing programs start from a user-defined initial input file, called a seed file. TabFuzz offers the possibility to generate such a file. This research shows that these generated files are just as effective as starting from a seed file.