Analyzing code corpora to improve the correctness and reliability of programs

Abstract

Bugs in software are commonplace, challenging, and expensive to deal with. One widely used direction is to use program analyses and reason about software to detect bugs in them. In recent years, the growth of areas like web application development and data analysis has produced large amounts of publicly available source code corpora, primarily written in dynamically typed languages, such as Python and JavaScript. It is challenging to reason about programs written in such languages because of the presence of dynamic features and the lack of statically declared types. This dissertation argues that, to build software developer tools for detecting and understanding bugs, it is worthwhile to analyze code corpora, which can uncover code idioms, runtime information, and natural language constructs such as comments. The dissertation is divided into three corpus-based approaches that support our argument. In the first part, we present static analyses over code corpora to generate new programs, to perform mutations on existing programs, and to generate data for effective training of neural models. We provide empirical evidence that the static analyses can scale to thousands of files and the trained models are useful in finding bugs in code. The second part of this dissertation presents dynamic analyses over code corpora. Our evaluations show that the analyses are effective in uncovering unexpected behaviors when multiple JavaScript libraries are included together and to generate data for training bug-finding neural models. Finally, we show that a corpus-based analysis can be useful for input reduction, which can help developers to find a smaller subset of an input that still triggers the required behavior. We envision that the current dissertation motivates future endeavors in corpus-based analysis to alleviate some of the challenges faced while ensuring the reliability and correctness of software. One direction is to combine data obtained by static and dynamic analyses over code corpora for training. Another direction is to use meta-learning approaches, where a model is trained using data extracted from the code corpora of one language and used for another language.