Merge pull request #121 from SMAT-Lab/Jarvx-patch-2

Update README.md

README.md

@@ -57,9 +57,9 @@ Scalpel is invited to be presented at EuroPython 2022. EuroPython is the oldest
 
 
 ## Acknowledgement
-This project has been inspired and supported by many existing works. If you think your work appears in this project but has not been mentioned yet, please let us know by any means.
+This project has been inspired by many existing works. If you think your work appears in this project but has not been mentioned yet, please let us know by any means.
 
-1. [Fuzzyingbook](https://www.fuzzingbook.org/) by Andreas Zeller, Rahul Gopinath, Marcel Böhme, Gordon Fraser, and Christian Holler.
+1. [Fuzzingbook](https://www.fuzzingbook.org/) by Andreas Zeller, Rahul Gopinath, Marcel Böhme, Gordon Fraser, and Christian Holler.
 2. [Debugging book](https://www.debuggingbook.org/) by Andreas Zeller.
 3. [StaticCFG](https://github.com/coetaur0/staticfg).
 4. [PyCG: Practical Call Graph Generation in Python](https://vitsalis.com/papers/pycg.pdf), ICSE 2021.
@@ -88,4 +88,4 @@ This project has been inspired and supported by many existing works. If you thin
 
 * Function 8: Call Graph Construction. A call graph depicts calling relationships between methods in a software program. It is a fundamental component in static flow analysis and can be leveraged in tasks such as profiling, vulnerability propagation, and refactoring. This module addresses the challenges brought by complicated features adopted in Python, such as higher-order functions and nested function definitions, to construct precise call graphs for given Python projects.
 
-* Function 9: Type Inference. Python, as a dynamically typed language, faces the problem of being hard to utilize the full power of traditional static analysis. This module infers the type information of all variables including function return values and function parameters in a Python program, making more sophisticated static analysis possible for Python. We utilize backward data-flow analysis and a set of heuristic rules to achieve high precision.
+* Function 9: Type Inference. Python, as a dynamically typed language, faces the problem of being hard to utilize the full power of traditional static analysis. This module infers the type information of all variables including function return values and function parameters in a Python program, making more sophisticated static analysis possible for Python. We utilize backward data-flow analysis and a set of heuristic rules to achieve high precision.