diff --git a/gstreamerAutotest.py b/gstreamerAutotest.py
index 6bcadc6..3e67040 100644
--- a/gstreamerAutotest.py
+++ b/gstreamerAutotest.py
@@ -224,4 +224,5 @@ def run_autotest():
         qualityDataframe.to_csv(f"qualityResults{encoder}.csv")
         latencyDataframe.to_csv(f"latencyDataframe{encoder}.csv")
 
-run_autotest()
\ No newline at end of file
+if __name__ == "__main__":
+    run_autotest()
\ No newline at end of file
diff --git a/latencyParse.py b/latencyParse.py
index ab74639..ecaa3dc 100644
--- a/latencyParse.py
+++ b/latencyParse.py
@@ -93,4 +93,5 @@ def getLatencyTable(filename):
     print(resultDf)
     return resultDf
 
-# getLatencyTable("latency_traces-x264enc-kpop-test-10.log")
+if __name__ == "__main__":
+    getLatencyTable("latency_traces-x264enc-kpop-test-10.log")
diff --git a/qa.py b/qa.py
index 3f1e901..1a4c032 100644
--- a/qa.py
+++ b/qa.py
@@ -79,40 +79,42 @@ def parse_quality_report(psnr_metrics, ssim_metrics):
     combined = combined.fillna(0)
     return combined
 
-# psnr, ssim = run_quality_check(
-#         "base-x264enc-kpop-test-10.yuv", 
-#         "encoded-x264enc-kpop-test-10.mp4", 
-#         "-pixel_format yuv420p -color_range tv -video_size 1920x1080 -framerate 23.98 "
-#     )
 
-# combined = parse_quality_report(
-#     psnr,
-#     ssim
-# )
+if __name__ == "__main__":
+    psnr, ssim = run_quality_check(
+            "base-x264enc-kpop-test-10.yuv", 
+            "encoded-x264enc-kpop-test-10.mp4", 
+            "-pixel_format yuv420p -color_range tv -video_size 1920x1080 -framerate 23.98 "
+        )
 
-# encoder = "x264enc"
-# profile = "main"
-# params = "bitrate=5000"
+    combined = parse_quality_report(
+        psnr,
+        ssim
+    )
 
-# columns = pd.MultiIndex.from_tuples(
-#         [(encoder, profile, params, col) for col in combined.columns]
-#     )
+    encoder = "x264enc"
+    profile = "main"
+    params = "bitrate=5000"
 
-# combined.columns = columns
+    columns = pd.MultiIndex.from_tuples(
+            [(encoder, profile, params, col) for col in combined.columns]
+        )
 
-# main_df = combined
-# profile = "baseline"
+    combined.columns = columns
 
-# combined2 = parse_quality_report(
-#     psnr,
-#     ssim
-# )
-# columns = pd.MultiIndex.from_tuples(
-#     [(encoder, profile, params, col) for col in combined2.columns]
-# )
-# combined2.columns = columns
-# main_df = pd.concat([main_df, combined2], axis=1)
-# print(main_df)
+    main_df = combined
+    profile = "baseline"
 
-# main_df.to_csv("quality_report.csv")
+    combined2 = parse_quality_report(
+        psnr,
+        ssim
+    )
+    columns = pd.MultiIndex.from_tuples(
+        [(encoder, profile, params, col) for col in combined2.columns]
+    )
+    combined2.columns = columns
+    main_df = pd.concat([main_df, combined2], axis=1)
+    print(main_df)
+
+    main_df.to_csv("quality_report.csv")