This study evaluates the efficiency of code generation by Large Language Models (LLMs) and measures their performance against human-crafted solutions using a dataset from Leetcode. We compare 18 LLMs, considering factors such as model temperature and success rate, and their impact on code performance. This research introduces a novel method for measuring and comparing the speed of LLM-generated code, revealing that LLMs produce code with comparable performance, irrespective of the adopted LLM. We also find that LLMs are capable of generating code that is, on average, more efficient than the code written by humans. The paper further discusses the use of Leetcode as a benchmarking dataset, the limitations imposed by potential data contamination, and the platform’s measurement reliability. We believe that our findings contribute to a better understanding of LLM capabilities in code generation and set the stage for future optimizations in the field.

Mobile apps are becoming complex software systems that must be developed quickly and evolve continuously to fit new user requirements and execution contexts. However, addressing these requirements may result in poor design choices, also known as antipatterns, which may incidentally degrade software quality and performance. Thus, the automatic detection and tracking of antipatterns in this apps are important activities in order to ease both maintenance and evolution. Moreover, they guide developers to refactor their applications and thus, to improve their quality. While antipatterns are well-known in object-oriented applications, their study in mobile applications is still in its infancy. In this paper, we analyze the evolution of mobile apps quality on 3, 568 versions of 106 popular Android applications downloaded from the Google Play Store. For this purpose, we use a tooled approach, called PAPRIKA, to identify 3 object-oriented and 4 Android-specific antipatterns from binaries of mobile apps, and to analyze their quality along evolutions.

Reducing the energy footprint of digital devices and software is a task challenging the research in Green IT. Researches have proposed approaches for energy management, ranging from reducing usage of software and hardware, compilators optimization, to server consolidation and software migration. However, optimizing the energy consumption requires knowledge of that said consumption. In particular, measuring the energy consumption of hardware and software is an important requirement for efficient energy strategies. In this review, we outline the different categories of approaches in energy measurements, and provide insights into example of each category. We draw recommendations from our review on requirements on how to efficiently measure energy consumption of devices and software.

Browser fingerprinting has emerged as a technique to track users without their consent. Unlike cookies, fingerprinting is a stateless technique that does not store any information on devices, but instead exploits unique combinations of attributes handed over freely by browsers. The uniqueness of fingerprints allows them to be used for identification. However, browser fingerprints change over time and the effectiveness of tracking users over longer durations has not been properly addressed. In this paper, we show that browser fingerprints tend to change frequently-from every few hours to days-due to, for example, software updates or configuration changes. Yet, despite these frequent changes, we show that browser fingerprints can still be linked, thus enabling long-term tracking. FP-STALKER is an approach to link browser fingerprint evolutions. It compares fingerprints to determine if they originate from the same browser. We created two variants of FP-STALKER, a rule-based variant that is faster, and a hybrid variant that exploits machine learning to boost accuracy. To evaluate FP-STALKER, we conduct an empirical study using 98,598 fingerprints we collected from 1, 905 distinct browser instances. We compare our algorithm with the state of the art and show that, on average, we can track browsers for 54.48 days, and 26 % of browsers can be tracked for more than 100 days.