Smart contracts underpin high-value ecosystems such as decentralized finance (DeFi), yet recurring vulnerabilities continue to cause losses worth billions of dollars. Although numerous security analyzers that detect such flaws exist, real-world attacks remain frequent, raising the question of whether these tools are truly effective or simply under-used due to low developer trust. Prior benchmarks have evaluated analyzers on synthetic or vulnerable-only contract datasets, limiting their ability to measure false positives, false negatives, and usability factors that drive adoption.

Smartphone application (app) developers measure the energy consumption of their apps to ensure that they do not consume excessive energy. However, existing techniques require developers to generate and execute test cases on expensive, sophisticated hardware. To address these challenges, we propose a static-analysis approach that estimates the energy consumption of API usage in an app, eliminating the need for test case execution. To instantiate our approach, we have profiled the energy consumption of the Swift SQLite API operations. Given a Swift app, we first scan it for uses of SQLite. We then combine that information with the measured energy profile to compute E-factor, an estimate of the energy consumption of the API usage in an app. To evaluate the usability of E-factor, we have calculated the E-factor of 56 real-world iOS apps. We have also compared the E-factor of 16 versions and 11 methods from 3 of those apps to their hardware-based energy measurements. Our findings show that E-factor positively correlates with the hardware-based energy measurements, indicating that E-factor is a practical estimate to compare the energy consumption difference in API usage across different versions of an app. Developers may also use E-factor to identify excessive energy-consuming methods in their apps and focus on optimizing them. Our approach is most useful in an Integrated Development Environment (IDE) or Continuous Integration (CI) pipeline, where developers receive energy consumption insights within milliseconds of making a code modification.

Machine learning, a branch of Artificial Intelligence, is now popular in software engineering community and is successfully used for problems like bug prediction, and software development effort estimation. Developers' understanding of machine learning, however, is not clear, and we require investigation to understand what educators should focus on, and how different online programming discussion communities can be more helpful. We conduct a study on Stack Overflow (SO) machine learning related posts using the SOTorrent dataset. We found that some machine learning topics are significantly more discussed than others, and others need more attention. We also found that topic generation with Latent Dirichlet Allocation (LDA) can suggest more appropriate tags that can make a machine learning post more visible and thus can help in receiving immediate feedback from sites like SO.