Transferring Tests Across Web Applications

When manually testing Web applications, humans can go with vague, yet general instructions, such as ``add the product to shopping cart and proceed to checkout''. Can we teach a robot to follow such instructions as well? We show how to leverage tests of other applications to guide test generation for new applications in the same domain: Given a test for payments on Amazon, we guide test generation on eBay towards payment functionality, exploiting the semantic similarity between UI elements across both applications. Evaluated on twelve Web apps in three domains, our approach allows for discovering deep functionality in a few minutes, where an undirected crawler would require days or weeks to accomplish the same task

Social media and bitcoin metrics: which words matter

We develop a new Data-Driven Phasic Word Identification (DDPWI) methodology to determine which words matter as the bitcoin pricing dynamic changes from one phase to another. With Google search volumes as a baseline, we find that Reddit submissions are both correlated with Google and have a comparable relationship with a variety of bitcoin metrics, using Spearman’s rho. Reddit provides complete access to the text of submissions. Rather than associating sentiment with market activity, we describe the DDPWI method for finding specific ‘price dynamic’ words associated with changes in the bitcoin pricing pattern through 2017 and 2018. We assess the significance of these changes using Wilcoxon Rank-Sum Tests with Bonferroni corrections. These price dynamic words are used to pull out associated words in the submissions thereby providing the context to their use. For example, the price dynamic word ‘ban’, which became significantly higher in frequency as prices fell, occurred in the context of both government regulation and internet companies banning cryptocurrency adverts. This approach could be used more generally to look at social media and discussion forums at a granular level identifying specific words that impact the metric under investigation rather than overall sentiment

Bundles: A Framework to Optimise Topic Analysis in Real-Time Chat Discourse.

Collaborative chat tools and large text corpora are ubiquitous in today’s world of real-time communication. As micro teams and start-ups adopt such tools, there is a need to understand the meaning (even at a high level) of chat conversations within collaborative teams. In this study, we propose a technique to segment chat conversations to increase the number of words available (19% on average) for text mining purposes. Using an open source dataset, we answer the question of whether having more words available for text mining can produce more useful information to the end user. Our technique can help microteams and start-ups with limited resources to efficiently model their conversations to afford a higher degree of readability and comprehension