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There has been an increased interest in the use of deep learning approaches for software analytics tasks. State-of-the-art techniques leverage modern deep learning techniques such as LSTMs, yielding competitive performance, albeit at the price of longer training times. Recently, Galke and Scherp [18] showed that at least for image recognition, a decades-old feedforward neural network can match the performance of modern deep learning techniques. This motivated us to try the same in the SE literature. Specifically, in this paper, we apply feedforward networks with some preprocessing to two analytics tasks: issue close time prediction, and vulnerability detection. We test the hypothesis laid by Galke and Scherp [18], that feedforward networks suffice for many analytics tasks (which we call, the "Old but Gold" hypothesis) for these two tasks. For three out of five datasets from these tasks, we achieve new high-water mark results (that out-perform the prior state-of-the-art results) and for a fourth data set, Old but Gold performed as well as the recent state of the art. Furthermore, the old but gold results were obtained orders of magnitude faster than prior work. For example, for issue close time, old but gold found good predictors in 90 seconds (as opposed to the newer methods, which took 6 hours to run). Our results supports the "Old but Gold" hypothesis and leads to the following recommendation: try simpler alternatives before more complex methods. At the very least, this will produce a baseline result against which researchers can compare some other, supposedly more sophisticated, approach. And in the best case, they will obtain useful results that are as good as anything else, in a small fraction of the effort. To support open science, all our scripts and data are available on-line at https://github.com/fastidiouschipmunk/simple., v2

Objective: We aim to learn potential novel cures for diseases from unstructured text sources. More specifically, we seek to extract drug-disease pairs of potential cures to diseases by a simple reasoning over the structure of spoken text. Materials and Methods: We use Google Cloud to transcribe podcast episodes of an NPR radio show. We then build a pipeline for systematically pre-processing the text to ensure quality input to the core classification model, which feeds to a series of post-processing steps for obtaining filtered results. Our classification model itself uses a language model pre-trained on PubMed text. The modular nature of our pipeline allows for ease of future developments in this area by substituting higher quality components at each stage of the pipeline. As a validation measure, we use ROBOKOP, an engine over a medical knowledge graph with only validated pathways, as a ground truth source for checking the existence of the proposed pairs. For the proposed pairs not found in ROBOKOP, we provide further verification using Chemotext. Results: We found 30.4% of our proposed pairs in the ROBOKOP database. For example, our model successfully identified that Omeprazole can help treat heartburn.We discuss the significance of this result, showing some examples of the proposed pairs. Discussion and Conclusion: The agreement of our results with the existing knowledge source indicates a step in the right direction. Given the plug-and-play nature of our framework, it is easy to add, remove, or modify parts to improve the model as necessary. We discuss the results showing some examples, and note that this is a potentially new line of research that has further scope to be explored. Although our approach was originally oriented on radio podcast transcripts, it is input-agnostic and could be applied to any source of textual data and to any problem of interest., initial submission