6 Repos
Resource profiling that identifies performance regressions by comparing current metrics against a known system baseline.
Distinct from CPU Profilers: Adds the baseline-comparison logic to standard CPU/memory profiling.
Explore 6 awesome GitHub repositories matching development tools & productivity · Baseline-Comparison Profiling. Refine with filters or upvote what's useful.
Coroot is an observability platform and Kubernetes performance monitor that utilizes eBPF to automatically collect metrics, logs, and traces without requiring manual code instrumentation. It functions as an OpenTelemetry trace analyzer and an LLM observability gateway, exposing system health data to large language models through the Model Context Protocol. The platform differentiates itself by combining automated root cause analysis and AI-driven diagnostics to investigate performance regressions. It also includes a cloud cost monitoring tool that attributes infrastructure spending to specifi
Identifies specific lines of code causing resource spikes by comparing current behavior against a system baseline.
This project is a comprehensive technical guide and diagnostic manual for analyzing memory, performance, and asynchronous behavior within Node.js applications. It provides detailed methods for asynchronous tracing, memory diagnostics, and performance analysis to resolve runtime errors and execution bottlenecks. The resource distinguishes itself by covering advanced diagnostic workflows, including the use of flame graphs for CPU profiling, the capture and comparison of heap snapshots for memory leak detection, and the mapping of asynchronous call stacks. It also provides technical guidance on
Generates differential flame graphs to highlight changes in CPU usage between different code versions.
WAFW00F allows one to identify and fingerprint Web Application Firewall (WAF) products protecting a website.
Compares benign and malicious request responses to detect WAF interference.
Criterion ist eine statistikbasierte Microbenchmarking-Bibliothek und ein Tool für Performance-Regressionen in Rust. Es bietet ein Framework zur Isolierung und Messung kleiner Code-Segmente und nutzt statistische Analysen, um Rauschen zu eliminieren und zuverlässige, wiederholbare Messungen der Ausführungsgeschwindigkeit zu gewährleisten. Das Tool zeichnet sich durch eine Performance-Visualisierungssuite aus, die HTML-Berichte und Diagramme generiert, um Leistungstrends und Durchsatz zu verfolgen. Es enthält ein System zum Vergleich aktueller Ausführungszeiten mit gespeicherten Baselines, um Leistungsabfälle zu identifizieren und zu verhindern. Die Bibliothek deckt die Messung asynchroner Funktionen, parametrisiertes Benchmarking für Input-Skalierung und die Berechnung des Code-Durchsatzes ab. Sie unterstützt zudem die Integration benutzerdefinierter Hardware-Metriken und Prozessor-Counter, um Low-Level-Daten während der Läufe zu erfassen. Die Automatisierung wird über eine CLI für das Filtern von Benchmarks und einen Validierungsmodus zur Überprüfung der erfolgreichen Ausführung innerhalb von CI-Pipelines unterstützt.
Identifies performance regressions by comparing current measurement results against stored baseline files.
This project is an algorithmic performance benchmarking tool and execution cycle simulator. It functions as a complexity analysis framework that measures code execution speed using simulated clock cycles to evaluate algorithmic efficiency against established performance baselines. The tool provides deterministic measurements of computational work and time complexity by tracking virtual clock cycles rather than real time. It utilizes a threshold-driven test suite to validate whether specific code implementations meet required performance cycle targets. The framework covers execution speed pro
Compares current execution costs against pre-defined cycle thresholds to identify performance regressions.
gprof2dot is a performance graph generator and visualizer that converts gprof GNU profiler execution profiles into Graphviz DOT files. It transforms raw profiler data into a directed graph to map function call hierarchies and identify software bottlenecks. The tool employs heuristic-based color mapping to highlight performance hotspots by assigning colors to nodes and edges based on execution time percentages. It also supports differential profile analysis, allowing for the comparison of two distinct execution graphs to identify changes in timing and call counts between runs. To improve visu
Identifies performance regressions by comparing current execution profiles against a known baseline.