Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

Pulsed Generation of Quantum Coherences and Non-classicality in Light-Matter Systems

We show that a pulsed stimulus can be used to generate many-body quantum coherences in light-matter systems of general size. Specifically, we calculate the exact real-time evolution of a driven, generic out-of-equilibrium system comprising an arbitrary number N qubits coupled to a global boson field. A novel form of dynamically-driven quantum coherence emerges for general N and without having to access the empirically challenging strong-coupling regime. Its properties depend on the speed of the changes in the stimulus. Non-classicalities arise within each subsystem that have eluded previous analyses. Our findings show robustness to losses and noise, and have potential functional implications at the systems level for a variety of nanosystems, including collections of N atoms, molecules, spins, or superconducting qubits in cavities -- and possibly even vibration-enhanced light harvesting processes in macromolecules.Comment: 9 pages, 4 figure

An Analysis of Energy Efficient Data Transfer between Mobile Device and Dedicated Server

This paper discusses research results with regard to energy-efficient transmission of serialised data between servers and mobile devices. A test environment was created in which the research authors primarily measured electricity consumption during communication between a mobile device and server. Numerical results were used to determine how well data serialisation was performed on a dedicated server and its effects on the power consumption of a mobile device. The time spent in data serialisation and the size of the serialised file were found to significantly influence energy consumption. Based on that fact, results have been used to create a mathematical model which was later introduced with functional forms. The main variables in those functional forms were time of serialisation and size of a serialised file. The data collected through this research has been used for an experimental API-CB Saver, which based on mathematical models chooses the most favourable manner of serialisation and compression in real time. The results collected during the tests show that the CBSaver-Api approach performs with greater energy efficiency than current techniques. Furthermore, with optimal selection of data serialisation type and compression level in real time the considered system shows better performance in power saving. According to the results, the API-CBSaver tests indicate the direction which one should take for the purposes of improving energy efficiency