We seek quantitative evidence (from dozens of Comptutational Science projects housed in Github) for 13 previously published conjectures about scientific software development in the literature. In all, we explore three groups of beliefs about (1) the nature of scientific challenges; (2) the implications of limitations of computer hardware; and (3) the cultural environment of scientific software development. We find that four cannot be assessed with respect to the Github data. Of the others, only three can be endorsed.

An anomaly detector, X-FLASH, identifies faulty TCP signatures in Scientific Workflows (SW). X-FLASH outperformed SOTA up to 40% relatively in recall within 30 evaluations. This implies (1) a proof-of-concept of tuning SE method for SW and (2) the necessity of tuning for future SW work.

Keeping track of and managing the self-admitted technical debts (SATDs) is important to maintaining a healthy software project. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs by first finding the "easy to find" SATDs automatically with close to 100% precision via a novel pattern recognition technique, then applying ML techniques to assist human experts in manually identifying the rest "hard to find" SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state of the art methods.

Standard automatic methods for recognizing problematic development commits can be greatly improved via the incremental application of human+artificial expertise. In this approach, called EMBLEM, an AI tool first explore the software development process to label commits that are most problematic. Humans then apply their expertise to check those labels (perhaps resulting in the AI updating the support vectors within their SVM learner).

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. An extensive empirical case study for hyperparameter tuning in defect prediction to questions the versatility of tuning’s usefulness while proposing future research and expanding the definition of tuning.

Novel combination method of LDA topic modeling and Fast Frugal Tree (depth of 4) to predict the severeness of software bug reports. Offers comparable performance but simpler (25%-250% smallerin scale) and faster (50 times faster) than the state-of-the-art text mining models (TFIDF+SVM and LDADE+SVM).