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+## Project Overview
+Pyinstrument is a profiler for python code that allows developers to measure the execution time of portions of python
+code, with the goal of locating performance bottlenecks. This allows developers to more easily optimize their code by
+drawing attention to the least optimized (read: slowest) sections. Pyinstrument's developers also focus on efficiency,
+minimizing performance overhead caused by the profiler in order to improve accuracy. Over the course of the semester,
+this project aims to apply the various testing techniques discussed in SWEN-777 to the pyinstrument repository, in order
+to measure and evaluate the extent to which the project meets key quality standards that we identify based on existing
+documentation. Those metrics are listed in the following section.
+
+## Key Quality Metrics
+
+- Accuracy; How close are pyinstrument's results to actual execution times?
+    - Measured by net and percent difference between pyinstrument's results and the results of another profiler and
+    between pyinstrument results on easily comparable pieces of code.
+
+- Efficiency; How close does the project get to maintianing minimal overhead while profiling code?
+    - Measured by difference in program execution time when using versus not using the profiler.
+
+- Maintainability; How easily can the project be updated/maintained by open-source contributors?
+    - Code Structure; How well-structured is the source code for the project?
+        - Measured by Lines of Code (LOC) per module/file, comment density, and possibly cyclomatic/cognitive complexity.
+    - Testability; How easily can the project be tested, and how thoroughly is the project currently tested?
+        - Measured by number of unit test cases per test suite and by test coverage (line coverage, branch coverage,
+        etc.)
+