Human-like summaries from heterogeneous and time-windowed software development artefacts

First Online: 02 September 2020Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically generated summaries the most useful when they are generated using word similarity and based on the eight most relevant software artefacts.Mahfouth Alghamdi, Christoph Treude, Markus Wagne

Big data framework for finding patterns in multi-market trading data

In the United States, multimarket trading is becoming very popular for investors, professionals and high-frequency traders. This research focuses on 13 exchanges and applies data mining algorithm, an unsupervised machine learning technique for discovering the relationships between stock exchanges. In this work, we used an association rule (FP-growth) algorithm for finding trading pattern in exchanges. Thirty days NYSE Trade and Quote (TAQ) data were used for these experiments. We implemented a big data framework of Spark clusters on the top of Hadoop to conduct the experiment. The rules and co-relations found in this work seems promising and can be used by the investors and traders to make a decision