SHF: SMALL: Automated Discovery of Cross-Language Program Behavior Inconsistency

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In the software industry, hundreds of programming languages exist, many of which programmers are expected to be proficient in. The common assumption has been that once a programmer knows one language, they can leverage concepts and knowledge already learned and easily pick up another programming language. Unfortunately, empirical studies find this process to be error-prone and ineffective due to imprecise mismatches between concepts and expressions across programming languages. This project develops techniques to ease the acquisition of knowledge for new programming languages by identifying and explaining how the behaviors of code in different languages relate. The anticipated result is that programmers will learn new languages faster and write code with fewer bugs. Beyond the general benefit of better-educated programmers, techniques for teaching computer programming are important in particular because programming is a crucial skill for a digitally literate society.This project will develop techniques to automatically identify incapabilities and potential misconceptions between two programming languages. Two main research tasks will be investigated for this project. The first task is to develop an approach for automatically identifying clusters of similar code based on dynamic behavior, likely invariants, observed side effects, and performance. Behavioral clusters are formed from snippets in multiple languages that produce the same outputs on the same inputs. Likely invariants from observed behavior are used to describe similarities and differences. The second task is to develop a technique to identify misconceptions that emerge when a programmer assumes code should behave the same but it does not. To identify misconceptions, the technique leverages the behavior clusters and characterizations from the code similarity analysis; code that looks similar but behaves differently in overt (behavior) or insidious (performance, side effects) ways are candidates. The technique will rank misconceptions based on probability of appearing and likely impact. Finally, the technique will use invariants, behavior, side effects and performance to form automated explanations of behavioral similarities and differences. Finally, these techniques and explanations will be applied for the benefit of two groups of real programmers: transfer students who know one language and need to learn a new one, and data scientists who work with many programming languages to complete their tasks. For programmers learning a new language, in student, professional, or hobby capacities, this work aims to increase the speed and reliability with which they acquire knowledge of the programming language.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.