Optimizing Parametric Dependencies for Incremental Performance Model Extraction

Architectural Support for Software Performance in Continuous Software Engineering: A Systematic Mapping Study

“…On the 48 papers, 28 papers (14 fully) investigated both software performance engineering and software architecture, that is, they offered a specific contribution in architecture-based performance engineering of software systems. More specifically, of the 28 papers, 14 papers (fully or partially) intensively used architectural/software models; some of them were devoted to model-based software engineering ([SP8], [SP63], [SP11], [SP14], [SP58], [SP9], [SP12], and [SP29]), and others are focused on model-driven software engineering ([SP48], [SP46], [SP53], [SP61], and [SP26]).…”

“…Only of the selected papers covered DevOps and only papers focused on the architectural support of performance engineering in the agile development process. Of them, ℎ papers combined software performance engineering, software architecture and DevOps ([SP50], [SP62], and [SP61]), whereas ℎ papers combined software performance engineering, software architecture and agile ([SP8], [SP63], and [SP15]).…”

“…They include methods and techniques aimed at evaluating or predicting the expected performance within a continuous (re-)engineering of the system. For instance, [SP63] presents an approach for continuous integration of performance model during agile development, by optimizing the learning of parametric dependencies; whereas [SP32] presents an approach that allows continuous performance adaptation through model predictive control. We can see that 22 selected papers aim to provide QoS, where the target is to satisfy different the requirements/properties for the overall quality of the system, including guaranteeing a certain level of performance.…”

Automated Detection of Software Performance Antipatterns in Java-Based Applications

“…In the literature, several approaches have been proposed for modeling, analyzing, and optimizing the performance of software applications [9], [10]. Two main directions have been pursued: (i) model-based performance analysis, i.e., performance models are built out of Java applications [11], [12] and used for predictions; (ii) application performance monitoring, i.e., tools that collect trace data for inspection [13], [14]. Motivated by the recent trend of integrating development (Dev) and operations (Ops) teams, processes, and tools [15], [16], [17], it is necessary that software engineers are aware of the performance evolution of their applications.…”