Abstract The code base of software projects evolves essentially through inserting and removing information to and from the source code. We can measure this evolution via the elements of infor-mation—tokens, words, nodes—of the respective representation of the code. In this work, we approach the measurement of the information content of the source code of open-source projects from an information-theoretic standpoint. Our focus is on the entropy of two funda-mental representations of code: tokens and abstract syntax tree nodes, from which we derive definitions of textual and structural entropy. We proceed with an empirical assessment where we evaluate the evolution patterns of the entropy of 95 actively maintained open source pro-jects. We calculate the statistical relationships between our derived entropy metrics and classic methods of measuring code complexity and learn that entropy may capture different dimen-sions of complexity than classic metrics. Finally, we conduct entropy-based anomaly detection of unusual changes to demonstrate that our approach may effectively recognise unusual source code change events with over 60% precision, and lay the groundwork for improvements to information-theoretic measurement of source code evolution, thus paving the way for a new approach to statically gauging program complexity throughout its development.

Misuse of cryptographic (crypto) APIs is a noteworthy cause of security vulnerabilities. For this reason, static analyzers were recently proposed for detecting crypto API misuses. They differ in strengths and weaknesses, and they might miss bugs. Motivated by the inherent limitations of static analyzers, this article reports on a study of runtime verification (RV) as a dynamic-analysis-based alternative for crypto API misuse detection. RV monitors program runs against formal specifications; it was shown to be effective and efficient for amplifying the bug-finding ability of software tests. We focus on the popular JCA crypto API and write 22 RV specifications based on expert-validated rules in a static analyzer. We monitor these specifications while running tests in five benchmarks. Lastly, we compare the accuracy of our RV-based approach, RVSec, with those of three state-of-the-art crypto API misuses detectors: CogniCrypt, CryptoGuard, and CryLogger. Results show that RVSec has higher accuracy in four benchmarks and is on par with CryptoGuard in the fifth. Overall, RVSec achieves an average${\boldsymbol{F}}_{1}$measure of 95%, compared with 83%, 78%, and 86% for CogniCrypt, CryptoGuard, and CryLogger, respectively. We highlight the strengths and limitations of these tools and show that RV is effective for detecting crypto API misuses. We also discuss how static and dynamic analysis can complement each other for detecting crypto API misuses.