Smart agriculture increasingly depends on advanced sensing and data analysis to optimize crop yields and support sustainable pest management. This work introduces an innovative approach to characterizing insect chemical communication, focusing on the Spodoptera genus, through the application of Information Theory Quantifiers (ITQs) and causality planes (Complexity–Entropy and Fisher–Shannon). Data were obtained from Electroantennography (GC-EAG) experiments, which record bioelectrical insect responses to volatile compounds via integrated sensors and microelectrodes. The methodology incorporates signal processing techniques such as ordinal patterns, sliding windows, and pre-processing stages to address the complexity and noise of time-series data. Results reveal that ITQs effectively capture distinct transitions from stochastic resting states to more organized active responses, highlighting their ability to detect and characterize complex chemical stimuli. These findings underscore the potential of ITQs as robust tools for identifying bioactive compounds, with direct applications in pest management and intelligent sensing within smart agriculture.

Regression Testing (RT) is a quality-assurance practice commonly adopted in the software industry to check if functionality remains intact after code changes. Test flakiness is a serious problem for RT. A test is said to be flaky when it non-deterministically passes or fails on a fixed environment. Prior work studied test flakiness primarily on Java programs. It is unclear, however, how problematic is test flakiness for software written in other programming languages. This paper reports on a study focusing on three central aspects of test flakiness: concentration, similarity, and cost. Considering concentration, our results show that, for any given programming language that we studied (C, Go, Java, JS, and Python), most issues could be explained by a small fraction of root causes (5/13 root causes cover 78.07% of the issues) and could be fixed by a relatively small fraction of fix strategies (10/23 fix strategies cover 85.20% of the issues). Considering similarity, although there were commonalities in root causes and fixes across languages (e.g., concurrency and async wait are common causes of flakiness in most languages), we also found important differences (e.g., flakiness due to improper release of resources are more common in C), suggesting that there is opportunity to fine tuning analysis tools. Considering cost, we found that issues related to flaky tests are resolved either very early once they are posted ($< $10 days), suggesting relevance, or very late ($>$100 days), suggesting irrelevance.