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ultima versao

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  1. __pycache__/util.cpython-310.pyc +0 -0
  2. _gerador_planilha.ipynb +423 -0
  3. _hiper_param.ipynb +145 -0
  4. _metrics generator.ipynb +149 -0
  5. app.py +37 -12
  6. database/tawos/README.md +48 -0
  7. database/tawos/deep/ALOY_deep-se.csv +0 -0
  8. database/tawos/deep/APSTUD_deep-se.csv +0 -0
  9. database/tawos/deep/CLI_deep-se.csv +0 -0
  10. database/tawos/deep/CLOV_deep-se.csv +0 -0
  11. database/tawos/deep/COMPASS_deep-se.csv +0 -0
  12. database/tawos/deep/CONFCLOUD_deep-se.csv +0 -0
  13. database/tawos/deep/CONFSERVER_deep-se.csv +0 -0
  14. database/tawos/deep/DAEMON_deep-se.csv +0 -0
  15. database/tawos/deep/DM_deep-se.csv +0 -0
  16. database/tawos/deep/DNN_deep-se.csv +0 -0
  17. database/tawos/deep/DURACLOUD_deep-se.csv +0 -0
  18. database/tawos/deep/EVG_deep-se.csv +0 -0
  19. database/tawos/deep/FAB_deep-se.csv +0 -0
  20. database/tawos/deep/MDL_deep-se.csv +0 -0
  21. database/tawos/deep/MESOS_deep-se.csv +0 -0
  22. database/tawos/deep/MULE_deep-se.csv +0 -0
  23. database/tawos/deep/NEXUS_deep-se.csv +0 -0
  24. database/tawos/deep/SERVER_deep-se.csv +0 -0
  25. database/tawos/deep/STL_deep-se.csv +0 -0
  26. database/tawos/deep/TIDOC_deep-se.csv +0 -0
  27. database/tawos/deep/TIMOB_deep-se.csv +0 -0
  28. database/tawos/deep/TISTUD_deep-se.csv +0 -0
  29. database/tawos/deep/XD_deep-se.csv +0 -0
  30. database/tawos/deep/metricas_NEXUS_MbR.csv +300 -0
  31. database/tawos/deep/metricas_NEXUS_NEOSP_SVR.csv +300 -0
  32. database/tawos/tfidf/ALOY_tfidf-se.csv +0 -0
  33. database/tawos/tfidf/APSTUD_tfidf-se.csv +0 -0
  34. database/tawos/tfidf/CLI_tfidf-se.csv +0 -0
  35. database/tawos/tfidf/CLOV_tfidf-se.csv +0 -0
  36. database/tawos/tfidf/COMPASS_tfidf-se.csv +0 -0
  37. database/tawos/tfidf/CONFCLOUD_tfidf-se.csv +0 -0
  38. database/tawos/tfidf/CONFSERVER_tfidf-se.csv +0 -0
  39. database/tawos/tfidf/DAEMON_tfidf-se.csv +0 -0
  40. database/tawos/tfidf/DM_tfidf-se.csv +0 -0
  41. database/tawos/tfidf/DNN_tfidf-se.csv +0 -0
  42. database/tawos/tfidf/DURACLOUD_tfidf-se.csv +0 -0
  43. database/tawos/tfidf/EVG_tfidf-se.csv +0 -0
  44. database/tawos/tfidf/FAB_tfidf-se.csv +0 -0
  45. database/tawos/tfidf/MDL_tfidf-se.csv +0 -0
  46. database/tawos/tfidf/MESOS_tfidf-se.csv +0 -0
  47. database/tawos/tfidf/MULE_tfidf-se.csv +0 -0
  48. database/tawos/tfidf/NEXUS_tfidf-se.csv +0 -0
  49. database/tawos/tfidf/SERVER_tfidf-se.csv +0 -0
  50. database/tawos/tfidf/STL_tfidf-se.csv +0 -0
__pycache__/util.cpython-310.pyc ADDED
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_gerador_planilha.ipynb ADDED
@@ -0,0 +1,423 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 9,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "import pandas as pd\n",
10
+ "import csv\n",
11
+ "import numpy as np\n",
12
+ "import pandas as pd\n",
13
+ "\n",
14
+ "def gerar_metricas(nome_projeto):\n",
15
+ " list_output_MbR = []\n",
16
+ " with open(\"metricas_{}_MbR.csv\".format(nome_projeto), \"r\") as arquivo:\n",
17
+ " arquivo_csv = csv.reader(arquivo)\n",
18
+ " for i, linha in enumerate(arquivo_csv):\n",
19
+ " list_output_MbR.append(float(linha[0]))\n",
20
+ " list_output_NEOSP_SVR = []\n",
21
+ " with open(\"metricas_{}_NEOSP_SVR.csv\".format(nome_projeto), \"r\") as arquivo:\n",
22
+ " arquivo_csv = csv.reader(arquivo)\n",
23
+ " for i, linha in enumerate(arquivo_csv):\n",
24
+ " list_output_NEOSP_SVR.append(float(linha[0]))\n",
25
+ " list_output_TFIDF_SVR = []\n",
26
+ " with open(\"metricas_{}_TFIDF.csv\".format(nome_projeto), \"r\") as arquivo:\n",
27
+ " arquivo_csv = csv.reader(arquivo)\n",
28
+ " for i, linha in enumerate(arquivo_csv):\n",
29
+ " list_output_TFIDF_SVR.append(float(linha[0]))\n",
30
+ " \n",
31
+ " list_results = [[\"MbR Regressor\", np.mean(list_output_MbR)], [\"NEOSP-SVR Regressor\", np.mean(list_output_NEOSP_SVR)], [\"TFIDF-SVR Regressor\", np.mean(list_output_TFIDF_SVR)]]\n",
32
+ " \n",
33
+ " df = pd.DataFrame(list_results, columns=[\"Model\", \"MAE\"])\n",
34
+ " \n",
35
+ " df_list_output_MbR = pd.DataFrame(list_output_MbR, columns=[\"MAE\"])\n",
36
+ " df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP_SVR, columns=[\"MAE\"])\n",
37
+ " df_list_output_TFIDF = pd.DataFrame(list_output_TFIDF_SVR, columns=[\"MAE\"])\n",
38
+ " \n",
39
+ " \n",
40
+ " return df_list_output_MbR, df_list_output_NEOSP, df_list_output_TFIDF"
41
+ ]
42
+ },
43
+ {
44
+ "cell_type": "code",
45
+ "execution_count": 10,
46
+ "metadata": {},
47
+ "outputs": [
48
+ {
49
+ "name": "stdout",
50
+ "output_type": "stream",
51
+ "text": [
52
+ " MAE\n",
53
+ "0 1.719630\n",
54
+ "1 1.619240\n",
55
+ "2 2.035138\n",
56
+ "3 2.714286\n",
57
+ "4 1.690284\n",
58
+ ".. ...\n",
59
+ "295 1.877688\n",
60
+ "296 1.825845\n",
61
+ "297 2.281874\n",
62
+ "298 1.448541\n",
63
+ "299 2.011329\n",
64
+ "\n",
65
+ "[300 rows x 1 columns]\n",
66
+ " MAE\n",
67
+ "0 3.991628\n",
68
+ "1 5.509540\n",
69
+ "2 4.276285\n",
70
+ "3 3.210280\n",
71
+ "4 4.419198\n",
72
+ ".. ...\n",
73
+ "295 4.515966\n",
74
+ "296 4.156574\n",
75
+ "297 4.072459\n",
76
+ "298 3.360661\n",
77
+ "299 4.133661\n",
78
+ "\n",
79
+ "[300 rows x 1 columns]\n",
80
+ " MAE\n",
81
+ "0 1.749430\n",
82
+ "1 1.615589\n",
83
+ "2 1.891128\n",
84
+ "3 1.352403\n",
85
+ "4 2.137931\n",
86
+ ".. ...\n",
87
+ "295 1.505617\n",
88
+ "296 1.497649\n",
89
+ "297 1.944096\n",
90
+ "298 1.594697\n",
91
+ "299 1.889890\n",
92
+ "\n",
93
+ "[300 rows x 1 columns]\n",
94
+ " MAE\n",
95
+ "0 6.520257\n",
96
+ "1 7.011882\n",
97
+ "2 3.508960\n",
98
+ "3 3.912252\n",
99
+ "4 6.778048\n",
100
+ ".. ...\n",
101
+ "295 5.067004\n",
102
+ "296 3.978298\n",
103
+ "297 4.770977\n",
104
+ "298 3.592259\n",
105
+ "299 3.753875\n",
106
+ "\n",
107
+ "[300 rows x 1 columns]\n",
108
+ " MAE\n",
109
+ "0 1.279750\n",
110
+ "1 1.747535\n",
111
+ "2 1.429980\n",
112
+ "3 1.367193\n",
113
+ "4 1.769231\n",
114
+ ".. ...\n",
115
+ "295 1.736029\n",
116
+ "296 1.297173\n",
117
+ "297 1.407627\n",
118
+ "298 1.742932\n",
119
+ "299 1.625247\n",
120
+ "\n",
121
+ "[300 rows x 1 columns]\n",
122
+ " MAE\n",
123
+ "0 1.484524\n",
124
+ "1 0.870833\n",
125
+ "2 1.362500\n",
126
+ "3 1.141270\n",
127
+ "4 1.118586\n",
128
+ ".. ...\n",
129
+ "295 1.162477\n",
130
+ "296 1.446631\n",
131
+ "297 1.257882\n",
132
+ "298 2.020915\n",
133
+ "299 1.351226\n",
134
+ "\n",
135
+ "[300 rows x 1 columns]\n",
136
+ " MAE\n",
137
+ "0 1.562142\n",
138
+ "1 1.121421\n",
139
+ "2 1.011506\n",
140
+ "3 1.309862\n",
141
+ "4 1.386479\n",
142
+ ".. ...\n",
143
+ "295 1.217656\n",
144
+ "296 1.166991\n",
145
+ "297 1.309732\n",
146
+ "298 1.546553\n",
147
+ "299 1.458637\n",
148
+ "\n",
149
+ "[300 rows x 1 columns]\n",
150
+ " MAE\n",
151
+ "0 3.183489\n",
152
+ "1 3.254141\n",
153
+ "2 2.820652\n",
154
+ "3 3.147516\n",
155
+ "4 3.724896\n",
156
+ ".. ...\n",
157
+ "295 3.343784\n",
158
+ "296 2.467027\n",
159
+ "297 2.586486\n",
160
+ "298 2.901351\n",
161
+ "299 2.793243\n",
162
+ "\n",
163
+ "[300 rows x 1 columns]\n",
164
+ " MAE\n",
165
+ "0 3.085758\n",
166
+ "1 2.960811\n",
167
+ "2 2.402817\n",
168
+ "3 3.173362\n",
169
+ "4 3.209479\n",
170
+ ".. ...\n",
171
+ "295 2.802939\n",
172
+ "296 2.611202\n",
173
+ "297 2.317784\n",
174
+ "298 3.191521\n",
175
+ "299 2.972561\n",
176
+ "\n",
177
+ "[300 rows x 1 columns]\n",
178
+ " MAE\n",
179
+ "0 0.817816\n",
180
+ "1 1.292632\n",
181
+ "2 0.798383\n",
182
+ "3 0.766996\n",
183
+ "4 0.811808\n",
184
+ ".. ...\n",
185
+ "295 0.764333\n",
186
+ "296 0.871069\n",
187
+ "297 0.830722\n",
188
+ "298 1.263252\n",
189
+ "299 0.915469\n",
190
+ "\n",
191
+ "[300 rows x 1 columns]\n",
192
+ " MAE\n",
193
+ "0 0.861718\n",
194
+ "1 1.440051\n",
195
+ "2 0.793965\n",
196
+ "3 1.079316\n",
197
+ "4 0.765291\n",
198
+ ".. ...\n",
199
+ "295 1.312637\n",
200
+ "296 0.746560\n",
201
+ "297 0.861487\n",
202
+ "298 0.844606\n",
203
+ "299 0.893629\n",
204
+ "\n",
205
+ "[300 rows x 1 columns]\n",
206
+ " MAE\n",
207
+ "0 0.651576\n",
208
+ "1 0.649636\n",
209
+ "2 0.624728\n",
210
+ "3 0.599720\n",
211
+ "4 0.670485\n",
212
+ ".. ...\n",
213
+ "295 0.625787\n",
214
+ "296 0.620482\n",
215
+ "297 0.617713\n",
216
+ "298 0.653711\n",
217
+ "299 0.671403\n",
218
+ "\n",
219
+ "[300 rows x 1 columns]\n",
220
+ " MAE\n",
221
+ "0 1.843216\n",
222
+ "1 1.947581\n",
223
+ "2 1.326613\n",
224
+ "3 1.818193\n",
225
+ "4 1.829548\n",
226
+ ".. ...\n",
227
+ "295 1.692674\n",
228
+ "296 1.968132\n",
229
+ "297 3.074359\n",
230
+ "298 1.361050\n",
231
+ "299 2.180586\n",
232
+ "\n",
233
+ "[300 rows x 1 columns]\n",
234
+ " MAE\n",
235
+ "0 8.977660\n",
236
+ "1 13.935714\n",
237
+ "2 11.211688\n",
238
+ "3 10.565220\n",
239
+ "4 10.296718\n",
240
+ ".. ...\n",
241
+ "295 12.521110\n",
242
+ "296 11.826106\n",
243
+ "297 9.696185\n",
244
+ "298 11.501992\n",
245
+ "299 10.755109\n",
246
+ "\n",
247
+ "[300 rows x 1 columns]\n",
248
+ " MAE\n",
249
+ "0 1.563933\n",
250
+ "1 1.527785\n",
251
+ "2 1.514647\n",
252
+ "3 1.419521\n",
253
+ "4 1.537474\n",
254
+ ".. ...\n",
255
+ "295 1.508380\n",
256
+ "296 1.713129\n",
257
+ "297 1.520726\n",
258
+ "298 1.499336\n",
259
+ "299 1.515071\n",
260
+ "\n",
261
+ "[300 rows x 1 columns]\n",
262
+ " MAE\n",
263
+ "0 3.124855\n",
264
+ "1 2.894180\n",
265
+ "2 3.055756\n",
266
+ "3 2.742524\n",
267
+ "4 2.910823\n",
268
+ ".. ...\n",
269
+ "295 2.968017\n",
270
+ "296 2.876247\n",
271
+ "297 2.786590\n",
272
+ "298 2.969128\n",
273
+ "299 2.820308\n",
274
+ "\n",
275
+ "[300 rows x 1 columns]\n",
276
+ " MAE\n",
277
+ "0 1.275430\n",
278
+ "1 0.953375\n",
279
+ "2 0.943311\n",
280
+ "3 1.077534\n",
281
+ "4 0.880661\n",
282
+ ".. ...\n",
283
+ "295 0.955650\n",
284
+ "296 0.951646\n",
285
+ "297 0.953232\n",
286
+ "298 0.975788\n",
287
+ "299 1.029818\n",
288
+ "\n",
289
+ "[300 rows x 1 columns]\n",
290
+ " MAE\n",
291
+ "0 2.097472\n",
292
+ "1 1.692843\n",
293
+ "2 1.335077\n",
294
+ "3 1.525819\n",
295
+ "4 1.529649\n",
296
+ ".. ...\n",
297
+ "295 1.723645\n",
298
+ "296 1.577294\n",
299
+ "297 1.154258\n",
300
+ "298 1.946014\n",
301
+ "299 1.708333\n",
302
+ "\n",
303
+ "[300 rows x 1 columns]\n",
304
+ " MAE\n",
305
+ "0 0.933591\n",
306
+ "1 0.871815\n",
307
+ "2 0.946461\n",
308
+ "3 0.765251\n",
309
+ "4 0.945946\n",
310
+ ".. ...\n",
311
+ "295 0.565508\n",
312
+ "296 0.833871\n",
313
+ "297 1.040323\n",
314
+ "298 1.303763\n",
315
+ "299 0.954301\n",
316
+ "\n",
317
+ "[300 rows x 1 columns]\n",
318
+ " MAE\n",
319
+ "0 2.833315\n",
320
+ "1 2.558738\n",
321
+ "2 3.003724\n",
322
+ "3 2.535398\n",
323
+ "4 2.797720\n",
324
+ ".. ...\n",
325
+ "295 2.425558\n",
326
+ "296 2.596320\n",
327
+ "297 2.665249\n",
328
+ "298 2.642077\n",
329
+ "299 3.175072\n",
330
+ "\n",
331
+ "[300 rows x 1 columns]\n",
332
+ " MAE\n",
333
+ "0 2.732684\n",
334
+ "1 2.229971\n",
335
+ "2 2.475378\n",
336
+ "3 2.495666\n",
337
+ "4 2.462650\n",
338
+ ".. ...\n",
339
+ "295 2.512257\n",
340
+ "296 2.619655\n",
341
+ "297 2.390718\n",
342
+ "298 2.241314\n",
343
+ "299 2.395140\n",
344
+ "\n",
345
+ "[300 rows x 1 columns]\n",
346
+ " MAE\n",
347
+ "0 2.290064\n",
348
+ "1 2.309586\n",
349
+ "2 2.167920\n",
350
+ "3 2.191076\n",
351
+ "4 2.249119\n",
352
+ ".. ...\n",
353
+ "295 2.204164\n",
354
+ "296 2.246984\n",
355
+ "297 2.521996\n",
356
+ "298 2.094582\n",
357
+ "299 2.248426\n",
358
+ "\n",
359
+ "[300 rows x 1 columns]\n",
360
+ " MAE\n",
361
+ "0 1.699304\n",
362
+ "1 1.956807\n",
363
+ "2 1.673685\n",
364
+ "3 1.788314\n",
365
+ "4 1.908642\n",
366
+ ".. ...\n",
367
+ "295 1.915542\n",
368
+ "296 2.274142\n",
369
+ "297 1.923457\n",
370
+ "298 1.721884\n",
371
+ "299 1.901150\n",
372
+ "\n",
373
+ "[300 rows x 1 columns]\n"
374
+ ]
375
+ }
376
+ ],
377
+ "source": [
378
+ "LIBRARIES = [\"ALOY\", \"APSTUD\", \"CLI\", \"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
379
+ " \"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
380
+ "\n",
381
+ "for lp in LIBRARIES:\n",
382
+ " df_list_output_MbR, df_list_output_NEOSP, df_list_output_TFIDF = gerar_metricas(lp)\n",
383
+ " print(df_list_output_MbR)"
384
+ ]
385
+ },
386
+ {
387
+ "cell_type": "code",
388
+ "execution_count": null,
389
+ "metadata": {},
390
+ "outputs": [],
391
+ "source": []
392
+ },
393
+ {
394
+ "cell_type": "code",
395
+ "execution_count": null,
396
+ "metadata": {},
397
+ "outputs": [],
398
+ "source": []
399
+ }
400
+ ],
401
+ "metadata": {
402
+ "kernelspec": {
403
+ "display_name": "Python 3",
404
+ "language": "python",
405
+ "name": "python3"
406
+ },
407
+ "language_info": {
408
+ "codemirror_mode": {
409
+ "name": "ipython",
410
+ "version": 3
411
+ },
412
+ "file_extension": ".py",
413
+ "mimetype": "text/x-python",
414
+ "name": "python",
415
+ "nbconvert_exporter": "python",
416
+ "pygments_lexer": "ipython3",
417
+ "version": "3.10.11"
418
+ },
419
+ "orig_nbformat": 4
420
+ },
421
+ "nbformat": 4,
422
+ "nbformat_minor": 2
423
+ }
_hiper_param.ipynb ADDED
@@ -0,0 +1,145 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 5,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "from sklearn.dummy import DummyRegressor\n",
10
+ "from nltk.corpus import stopwords\n",
11
+ "from textblob import TextBlob\n",
12
+ "import textstat\n",
13
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
14
+ "from sklearn import svm\n",
15
+ "from sklearn.linear_model import LinearRegression\n",
16
+ "from sklearn.feature_selection import SelectKBest\n",
17
+ "import pandas as pd\n",
18
+ "from util import escape_tags_and_content, escape_tags, escape_strings, escape_links, escape_hex_character_codes, escape_punctuation_boundaries, escape_odd_spaces\n",
19
+ "from sklearn.model_selection import RepeatedKFold\n",
20
+ "from sklearn.svm import SVR\n",
21
+ "from sklearn.model_selection import GridSearchCV\n",
22
+ "import numpy as np\n",
23
+ "import util"
24
+ ]
25
+ },
26
+ {
27
+ "cell_type": "code",
28
+ "execution_count": 6,
29
+ "metadata": {},
30
+ "outputs": [],
31
+ "source": [
32
+ "def gerar_metricas(project_name):\n",
33
+ "\n",
34
+ " # carregando os dados\n",
35
+ " df = pd.read_csv(\"database\\\\tawos\\\\deep\\\\{}_deep-se.csv\".format(project_name))\n",
36
+ "\n",
37
+ " # criação de uma nova coluna\n",
38
+ " df[\"context\"] = df[\"title\"] + df[\"description\"]\n",
39
+ "\n",
40
+ " # pré-processamento\n",
41
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_tags_and_content(x))\n",
42
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_tags(x))\n",
43
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_strings(x))\n",
44
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_links(x))\n",
45
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_hex_character_codes(x))\n",
46
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_punctuation_boundaries(x))\n",
47
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_odd_spaces(x))\n",
48
+ "\n",
49
+ " # removendo stop-words\n",
50
+ " stop = stopwords.words('english')\n",
51
+ " df['context'] = df['context'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
52
+ "\n",
53
+ " # renomeando as colunas porque senão dá um problema com a extração de features do NEOSP\n",
54
+ " df = df.rename(columns={ \"issuekey\": \"issuekey_\", \"created\": \"created_\", \"description\": \"description_\", \"title\": \"title_\", \"context\": \"context_\", \"storypoint\": \"storypoint_\"})\n",
55
+ " y = df[\"storypoint_\"]\n",
56
+ " df = df.drop(columns=['storypoint_'])\n",
57
+ "\n",
58
+ " # 5º coluna -> extração das features para o neosp\n",
59
+ " df[\"gunning_fog_\"] = df['context_'].apply(textstat.gunning_fog)\n",
60
+ " df[\"flesch_reading_ease_\"] = df['context_'].apply(textstat.flesch_reading_ease)\n",
61
+ " df[\"flesch_kincaid_grade_\"] = df['context_'].apply(textstat.flesch_kincaid_grade)\n",
62
+ " df[\"smog_index_\"] = df['context_'].apply(textstat.smog_index)\n",
63
+ " df[\"coleman_liau_index_\"] = df['context_'].apply(textstat.coleman_liau_index)\n",
64
+ " df[\"automated_readability_index_\"] = df['context_'].apply(textstat.automated_readability_index)\n",
65
+ " df[\"dale_chall_readability_score_\"] = df['context_'].apply(textstat.dale_chall_readability_score)\n",
66
+ " df[\"difficult_words_\"] = df['context_'].apply(textstat.difficult_words)\n",
67
+ " df[\"linsear_write_formula_\"] = df['context_'].apply(textstat.linsear_write_formula)\n",
68
+ " df[\"polarity_\"] = df[\"context_\"].apply(lambda x: TextBlob(x).sentiment.polarity)\n",
69
+ " df[\"subjectivity_\"] = df[\"context_\"].apply(lambda x: TextBlob(x).sentiment.subjectivity)\n",
70
+ " # 16º colunas\n",
71
+ "\n",
72
+ " # Extração das features para o TFIDF\n",
73
+ " vectorizer = TfidfVectorizer()\n",
74
+ " X_vec = vectorizer.fit_transform(df[\"context_\"])\n",
75
+ "\n",
76
+ " df_vec = pd.DataFrame(data = X_vec.toarray(), columns = vectorizer.get_feature_names_out())\n",
77
+ "\n",
78
+ " # Juntando as features do neosp com o tfidf\n",
79
+ " df = df.join(df_vec)\n",
80
+ " X = df\n",
81
+ " \n",
82
+ " grid = GridSearchCV(\n",
83
+ " estimator=SVR(kernel='rbf'),\n",
84
+ " param_grid={\n",
85
+ " 'C': [1.1, 5.4, 170, 1001],\n",
86
+ " 'epsilon': [0.0003, 0.007, 0.0109, 0.019, 0.14, 0.05, 8, 0.2, 3, 2, 7],\n",
87
+ " 'gamma': [0.7001, 0.008, 0.001, 3.1, 1, 1.3, 5]\n",
88
+ " }, \n",
89
+ " cv=10, scoring='neg_mean_squared_error', verbose=0, n_jobs=-1)\n",
90
+ "\n",
91
+ " #print the best parameters from all possible combinations\n",
92
+ " grid.fit(X[X.columns[5:16]], y)\n",
93
+ " print(grid.best_params_)"
94
+ ]
95
+ },
96
+ {
97
+ "cell_type": "code",
98
+ "execution_count": 7,
99
+ "metadata": {},
100
+ "outputs": [
101
+ {
102
+ "name": "stdout",
103
+ "output_type": "stream",
104
+ "text": [
105
+ "{'C': 5.4, 'epsilon': 0.2, 'gamma': 5}\n",
106
+ "{'C': 1.1, 'epsilon': 2, 'gamma': 0.7001}\n"
107
+ ]
108
+ }
109
+ ],
110
+ "source": [
111
+ "import pandas as pd\n",
112
+ "\n",
113
+ "LIBRARIES = [\"ALOY\", \"CLI\"]\n",
114
+ " #\"APSTUD\", \n",
115
+ " #\"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
116
+ " #\"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
117
+ "\n",
118
+ "for lp in LIBRARIES:\n",
119
+ " gerar_metricas(lp)"
120
+ ]
121
+ }
122
+ ],
123
+ "metadata": {
124
+ "kernelspec": {
125
+ "display_name": "Python 3",
126
+ "language": "python",
127
+ "name": "python3"
128
+ },
129
+ "language_info": {
130
+ "codemirror_mode": {
131
+ "name": "ipython",
132
+ "version": 3
133
+ },
134
+ "file_extension": ".py",
135
+ "mimetype": "text/x-python",
136
+ "name": "python",
137
+ "nbconvert_exporter": "python",
138
+ "pygments_lexer": "ipython3",
139
+ "version": "3.10.11"
140
+ },
141
+ "orig_nbformat": 4
142
+ },
143
+ "nbformat": 4,
144
+ "nbformat_minor": 2
145
+ }
_metrics generator.ipynb ADDED
@@ -0,0 +1,149 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "code",
5
+ "execution_count": 13,
6
+ "metadata": {},
7
+ "outputs": [],
8
+ "source": [
9
+ "from sklearn.dummy import DummyRegressor\n",
10
+ "from nltk.corpus import stopwords\n",
11
+ "from textblob import TextBlob\n",
12
+ "import textstat\n",
13
+ "from sklearn.feature_extraction.text import TfidfVectorizer\n",
14
+ "from sklearn import svm\n",
15
+ "from sklearn.linear_model import LinearRegression\n",
16
+ "from sklearn.feature_selection import SelectKBest\n",
17
+ "import pandas as pd\n",
18
+ "from util import escape_tags_and_content, escape_tags, escape_strings, escape_links, escape_hex_character_codes, escape_punctuation_boundaries, escape_odd_spaces\n",
19
+ "from sklearn.model_selection import cross_val_score\n",
20
+ "from sklearn.model_selection import RepeatedKFold\n",
21
+ "from sklearn.pipeline import make_pipeline\n",
22
+ "from sklearn.preprocessing import StandardScaler\n",
23
+ "from sklearn.feature_selection import f_classif, f_regression"
24
+ ]
25
+ },
26
+ {
27
+ "cell_type": "code",
28
+ "execution_count": 14,
29
+ "metadata": {},
30
+ "outputs": [],
31
+ "source": [
32
+ "def gerar_metricas(project_name):\n",
33
+ "\n",
34
+ " # carregando os dados\n",
35
+ " df = pd.read_csv(\"database\\\\tawos\\\\deep\\\\{}_deep-se.csv\".format(project_name))\n",
36
+ "\n",
37
+ " # criação de uma nova coluna\n",
38
+ " df[\"context\"] = df[\"title\"] + df[\"description\"]\n",
39
+ "\n",
40
+ " # pré-processamento\n",
41
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_tags_and_content(x))\n",
42
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_tags(x))\n",
43
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_strings(x))\n",
44
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_links(x))\n",
45
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_hex_character_codes(x))\n",
46
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_punctuation_boundaries(x))\n",
47
+ " df[\"context\"] = df[\"context\"].apply(lambda x: escape_odd_spaces(x))\n",
48
+ "\n",
49
+ " # removendo stop-words\n",
50
+ " stop = stopwords.words('english')\n",
51
+ " df['context'] = df['context'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
52
+ "\n",
53
+ " # renomeando as colunas porque senão dá um problema com a extração de features do NEOSP\n",
54
+ " df = df.rename(columns={ \"issuekey\": \"issuekey_\", \"created\": \"created_\", \"description\": \"description_\", \"title\": \"title_\", \"context\": \"context_\", \"storypoint\": \"storypoint_\"})\n",
55
+ " y = df[\"storypoint_\"]\n",
56
+ " df = df.drop(columns=['storypoint_'])\n",
57
+ "\n",
58
+ " # 5º coluna -> extração das features para o neosp\n",
59
+ " df[\"gunning_fog_\"] = df['context_'].apply(textstat.gunning_fog)\n",
60
+ " df[\"flesch_reading_ease_\"] = df['context_'].apply(textstat.flesch_reading_ease)\n",
61
+ " df[\"flesch_kincaid_grade_\"] = df['context_'].apply(textstat.flesch_kincaid_grade)\n",
62
+ " df[\"smog_index_\"] = df['context_'].apply(textstat.smog_index)\n",
63
+ " df[\"coleman_liau_index_\"] = df['context_'].apply(textstat.coleman_liau_index)\n",
64
+ " df[\"automated_readability_index_\"] = df['context_'].apply(textstat.automated_readability_index)\n",
65
+ " df[\"dale_chall_readability_score_\"] = df['context_'].apply(textstat.dale_chall_readability_score)\n",
66
+ " df[\"difficult_words_\"] = df['context_'].apply(textstat.difficult_words)\n",
67
+ " df[\"linsear_write_formula_\"] = df['context_'].apply(textstat.linsear_write_formula)\n",
68
+ " df[\"polarity_\"] = df[\"context_\"].apply(lambda x: TextBlob(x).sentiment.polarity)\n",
69
+ " df[\"subjectivity_\"] = df[\"context_\"].apply(lambda x: TextBlob(x).sentiment.subjectivity)\n",
70
+ " # 16º colunas\n",
71
+ "\n",
72
+ " # Extração das features para o TFIDF\n",
73
+ " vectorizer = TfidfVectorizer()\n",
74
+ " X_vec = vectorizer.fit_transform(df[\"context_\"])\n",
75
+ "\n",
76
+ " df_vec = pd.DataFrame(data = X_vec.toarray(), columns = vectorizer.get_feature_names_out())\n",
77
+ "\n",
78
+ " # Juntando as features do neosp com o tfidf\n",
79
+ " df = df.join(df_vec)\n",
80
+ " X = df\n",
81
+ "\n",
82
+ " results = list()\n",
83
+ "\n",
84
+ " rkf = RepeatedKFold(n_splits=10, n_repeats=30, random_state=2652124)\n",
85
+ " \n",
86
+ " #model = DummyRegressor(strategy=\"mean\")\n",
87
+ " #results.append(cross_val_score(model, X[X.columns[5:16]], y, cv = rkf, scoring=\"neg_mean_absolute_error\"))\n",
88
+ " \n",
89
+ " model = make_pipeline(SelectKBest(f_regression, k=50), StandardScaler(), svm.SVR())\n",
90
+ " results.append(cross_val_score(model, X[X.columns[16:]], y, cv = rkf, scoring=\"neg_mean_absolute_error\"))\n",
91
+ " \n",
92
+ " list_results_TFIDF_MbR = results[0]\n",
93
+ " df_results_MAE_TFIDF = pd.DataFrame(list_results_TFIDF_MbR, columns = [\"MAE\"])\n",
94
+ " df_results_MAE_TFIDF = df_results_MAE_TFIDF.apply(lambda x: x*-1)\n",
95
+ " df_results_MAE_TFIDF.to_csv(\"metricas_{}_TFIDF.csv\".format(project_name),index = False, header=False)\n",
96
+ " \n",
97
+ " #list_results = [ [\"MbR\", results[0].mean()*-1], [\"NEOSP-SVR\", results[1].mean()*-1] ]\n",
98
+ " #df = pd.DataFrame(list_results, columns = [\"Model\",\"MAE\"])\n",
99
+ "\n",
100
+ " #list_results_MAE_MbR = results[0]\n",
101
+ " #df_results_MAE_MbR = pd.DataFrame(list_results_MAE_MbR, columns = [\"MAE\"])\n",
102
+ " #df_results_MAE_MbR = df_results_MAE_MbR.apply(lambda x: x*-1)\n",
103
+ " #df_results_MAE_MbR.to_csv(\"metricas_{}_MbR.csv\".format(project_name),index = False, header=False)\n",
104
+ "\n",
105
+ " #list_results_MAE_NEOSP = results[1]\n",
106
+ " #df_results_MAE_NEOSP = pd.DataFrame(list_results_MAE_NEOSP, columns = [\"MAE\"])\n",
107
+ " #df_results_MAE_NEOSP = df_results_MAE_NEOSP.apply(lambda x: x*-1)\n",
108
+ " #df_results_MAE_NEOSP.to_csv(\"metricas_{}_NEOSP_SVR.csv\".format(project_name), index = False, header=False)"
109
+ ]
110
+ },
111
+ {
112
+ "cell_type": "code",
113
+ "execution_count": 15,
114
+ "metadata": {},
115
+ "outputs": [],
116
+ "source": [
117
+ "import pandas as pd\n",
118
+ "\n",
119
+ "LIBRARIES = [\"ALOY\", \"APSTUD\", \"CLI\", \"CLOV\", \"COMPASS\", \"CONFCLOUD\", \"CONFSERVER\", \"DAEMON\", \"DM\", \"DNN\", \"DURACLOUD\", \"EVG\", \"FAB\", \n",
120
+ " \"MDL\", \"MESOS\" ,\"MULE\", \"NEXUS\", \"SERVER\", \"STL\", \"TIDOC\", \"TIMOB\", \"TISTUD\", \"XD\"]\n",
121
+ "\n",
122
+ "for lp in LIBRARIES:\n",
123
+ " gerar_metricas(lp)"
124
+ ]
125
+ }
126
+ ],
127
+ "metadata": {
128
+ "kernelspec": {
129
+ "display_name": "Python 3",
130
+ "language": "python",
131
+ "name": "python3"
132
+ },
133
+ "language_info": {
134
+ "codemirror_mode": {
135
+ "name": "ipython",
136
+ "version": 3
137
+ },
138
+ "file_extension": ".py",
139
+ "mimetype": "text/x-python",
140
+ "name": "python",
141
+ "nbconvert_exporter": "python",
142
+ "pygments_lexer": "ipython3",
143
+ "version": "3.10.11"
144
+ },
145
+ "orig_nbformat": 4
146
+ },
147
+ "nbformat": 4,
148
+ "nbformat_minor": 2
149
+ }
app.py CHANGED
@@ -9,11 +9,15 @@ import matplotlib.gridspec as gridspec
9
  LIBRARIES = ["ALOY", "APSTUD", "CLI", "CLOV", "COMPASS", "CONFCLOUD", "CONFSERVER", "DAEMON", "DM", "DNN", "DURACLOUD", "EVG", "FAB",
10
  "MDL", "MESOS" ,"MULE", "NEXUS", "SERVER", "STL", "TIDOC", "TIMOB", "TISTUD", "XD"]
11
 
12
- def grafico(list_output_MbR, list_output_NEOSP, nome_projeto, pip_choices):
13
- list_results = [["MbR Regressor", np.mean(list_output_MbR)], ["NEOSP-SVR Regressor", np.mean(list_output_NEOSP)]]
 
14
  df = pd.DataFrame(list_results, columns=["Model", "MAE"])
 
15
  df_list_output_MbR = pd.DataFrame(list_output_MbR, columns=["MAE"])
16
  df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP, columns=["MAE"])
 
 
17
  fig, ax = plt.subplots(2, 2)
18
 
19
  G = gridspec.GridSpec(2, 2)
@@ -31,16 +35,21 @@ def grafico(list_output_MbR, list_output_NEOSP, nome_projeto, pip_choices):
31
  axes_1.scatter(range(1,51), df_list_output_MbR["MAE"].loc[1:50], label="MbR Regressor", color="red", alpha=0.5,)
32
  if "NEOSP-SVR Regressor" in pip_choices:
33
  axes_1.scatter(range(1,51), df_list_output_NEOSP["MAE"].loc[1:50], label="NEOSP-SVR Regressor", color = "blue", alpha=0.5)
 
 
34
 
35
  # ax2
36
  axes_2.set_ylabel("MAE Médio")
37
  axes_2.set_xlabel("Modelos")
38
  if "MbR Regressor" in pip_choices:
39
  graf1 = axes_2.bar(df["Model"].iloc[[0]], df["MAE"].iloc[[0]], color="red", alpha=0.5)
40
- axes_2.bar_label(graf1, fmt="%.01f", size=10, label_type="edge")
41
  if "NEOSP-SVR Regressor" in pip_choices:
42
  graf2 = axes_2.bar(df["Model"].iloc[[1]], df["MAE"].iloc[[1]], color = "blue", alpha=0.5)
43
- axes_2.bar_label(graf2, fmt="%.01f", size=10, label_type="edge")
 
 
 
44
 
45
  # ax3
46
  axes_3.set_xlabel("MAE")
@@ -49,6 +58,8 @@ def grafico(list_output_MbR, list_output_NEOSP, nome_projeto, pip_choices):
49
  axes_3.hist(df_list_output_MbR["MAE"], color="red", alpha=0.5)
50
  if "NEOSP-SVR Regressor" in pip_choices:
51
  axes_3.hist(df_list_output_NEOSP["MAE"], color="blue", alpha=0.5)
 
 
52
 
53
  # graficos geral
54
  fig.set_figwidth(15)
@@ -56,12 +67,18 @@ def grafico(list_output_MbR, list_output_NEOSP, nome_projeto, pip_choices):
56
  fig.suptitle("Projeto {}".format(nome_projeto))
57
  # text
58
 
59
- resultado = ""
60
- if (pip_choices == ["NEOSP-SVR Regressor", "MbR Regressor"]) or (pip_choices == ["MbR Regressor", "NEOSP-SVR Regressor"]):
61
- res = wilcoxon(list_output_MbR, list_output_NEOSP)
62
- resultado = "MbR vs. NEOSP-SVR => Statistics: {} | valor-p: {}".format(res.statistic, res.pvalue)
 
 
 
 
 
 
63
 
64
- return gr.update(value=plt, visible=True), gr.update(value=resultado, visible=True)
65
 
66
 
67
  def create_pip_plot(libraries, pip_choices):
@@ -76,7 +93,13 @@ def create_pip_plot(libraries, pip_choices):
76
  arquivo_csv = csv.reader(arquivo)
77
  for i, linha in enumerate(arquivo_csv):
78
  list_output_NEOSP_SVR.append(float(linha[0]))
79
- return grafico(list_output_MbR, list_output_NEOSP_SVR, nome_projeto, pip_choices)
 
 
 
 
 
 
80
 
81
 
82
  demo = gr.Blocks()
@@ -88,7 +111,7 @@ with demo:
88
  libraries = gr.Dropdown(choices=LIBRARIES, label="Projeto", value="ALOY")
89
  with gr.Column():
90
  gr.Markdown("## Gráficos")
91
- pip = gr.CheckboxGroup(choices=["MbR Regressor", "NEOSP-SVR Regressor"], label="Modelos Preditivos")
92
  # stars = gr.CheckboxGroup(choices=["Stars", "Week over Week"], label="")
93
  # issues = gr.CheckboxGroup(choices=["Issue", "Exclude org members", "week over week"], label="")
94
  with gr.Row():
@@ -96,11 +119,13 @@ with demo:
96
  with gr.Row():
97
  with gr.Column():
98
  star_plot = gr.Text(visible=False, label="Wilcoxon Test")
 
 
99
  pip_plot = gr.Plot(visible=False)
100
 
101
  # issue_plot = gr.Plot(visible=False)
102
 
103
- fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=[pip_plot, star_plot])
104
  #fetch.click(create_star_plot, inputs=[libraries, pip], outputs=star_plot)
105
  # fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)
106
 
 
9
  LIBRARIES = ["ALOY", "APSTUD", "CLI", "CLOV", "COMPASS", "CONFCLOUD", "CONFSERVER", "DAEMON", "DM", "DNN", "DURACLOUD", "EVG", "FAB",
10
  "MDL", "MESOS" ,"MULE", "NEXUS", "SERVER", "STL", "TIDOC", "TIMOB", "TISTUD", "XD"]
11
 
12
+ def grafico(list_output_MbR, list_output_NEOSP, list_output_TFIDF, nome_projeto, pip_choices):
13
+ list_results = [["MbR Regressor", np.mean(list_output_MbR)], ["NEOSP-SVR Regressor", np.mean(list_output_NEOSP)], ["TFIDF-SVR Regressor", np.mean(list_output_TFIDF)]]
14
+
15
  df = pd.DataFrame(list_results, columns=["Model", "MAE"])
16
+
17
  df_list_output_MbR = pd.DataFrame(list_output_MbR, columns=["MAE"])
18
  df_list_output_NEOSP = pd.DataFrame(list_output_NEOSP, columns=["MAE"])
19
+ df_list_output_TFIDF = pd.DataFrame(list_output_TFIDF, columns=["MAE"])
20
+
21
  fig, ax = plt.subplots(2, 2)
22
 
23
  G = gridspec.GridSpec(2, 2)
 
35
  axes_1.scatter(range(1,51), df_list_output_MbR["MAE"].loc[1:50], label="MbR Regressor", color="red", alpha=0.5,)
36
  if "NEOSP-SVR Regressor" in pip_choices:
37
  axes_1.scatter(range(1,51), df_list_output_NEOSP["MAE"].loc[1:50], label="NEOSP-SVR Regressor", color = "blue", alpha=0.5)
38
+ if "TFIDF-SVR Regressor" in pip_choices:
39
+ axes_1.scatter(range(1,51), df_list_output_TFIDF["MAE"].loc[1:50], label="TFIDF-SVR Regressor", color = "green", alpha=0.5)
40
 
41
  # ax2
42
  axes_2.set_ylabel("MAE Médio")
43
  axes_2.set_xlabel("Modelos")
44
  if "MbR Regressor" in pip_choices:
45
  graf1 = axes_2.bar(df["Model"].iloc[[0]], df["MAE"].iloc[[0]], color="red", alpha=0.5)
46
+ axes_2.bar_label(graf1, fmt="%.03f", size=10, label_type="edge")
47
  if "NEOSP-SVR Regressor" in pip_choices:
48
  graf2 = axes_2.bar(df["Model"].iloc[[1]], df["MAE"].iloc[[1]], color = "blue", alpha=0.5)
49
+ axes_2.bar_label(graf2, fmt="%.03f", size=10, label_type="edge")
50
+ if "TFIDF-SVR Regressor" in pip_choices:
51
+ graf3 = axes_2.bar(df["Model"].iloc[[2]], df["MAE"].iloc[[2]], color = "green", alpha=0.5)
52
+ axes_2.bar_label(graf3, fmt="%.03f", size=10, label_type="edge")
53
 
54
  # ax3
55
  axes_3.set_xlabel("MAE")
 
58
  axes_3.hist(df_list_output_MbR["MAE"], color="red", alpha=0.5)
59
  if "NEOSP-SVR Regressor" in pip_choices:
60
  axes_3.hist(df_list_output_NEOSP["MAE"], color="blue", alpha=0.5)
61
+ if "TFIDF-SVR Regressor" in pip_choices:
62
+ axes_3.hist(df_list_output_TFIDF["MAE"], color="green", alpha=0.5)
63
 
64
  # graficos geral
65
  fig.set_figwidth(15)
 
67
  fig.suptitle("Projeto {}".format(nome_projeto))
68
  # text
69
 
70
+ resultado, resultado2 = "", ""
71
+ #if (pip_choices == ["NEOSP-SVR Regressor", "MbR Regressor"]) or (pip_choices == ["MbR Regressor", "NEOSP-SVR Regressor"]):
72
+ res = wilcoxon(list_output_NEOSP, list_output_MbR)
73
+ resultado = "NEOSP-SVR vs. MbR => Statistics: {} | valor-p: {}".format(res.statistic, res.pvalue)
74
+ #if (pip_choices == ["NEOSP-SVR Regressor", "MbR Regressor"]) or (pip_choices == ["MbR Regressor", "NEOSP-SVR Regressor"]):
75
+ res = wilcoxon(list_output_NEOSP, list_output_TFIDF)
76
+ resultado2 = "NEOSP-SVR vs. TFIDF-SVR => Statistics: {} | valor-p: {}".format(res.statistic, res.pvalue)
77
+
78
+ res = wilcoxon(list_output_TFIDF, list_output_MbR)
79
+ resultado3 = "TFIDF-SVR vs. MbR => Statistics: {} | valor-p: {}".format(res.statistic, res.pvalue)
80
 
81
+ return gr.update(value=plt, visible=True), gr.update(value=resultado, visible=True), gr.update(value=resultado2, visible=True), gr.update(value=resultado3, visible=True)
82
 
83
 
84
  def create_pip_plot(libraries, pip_choices):
 
93
  arquivo_csv = csv.reader(arquivo)
94
  for i, linha in enumerate(arquivo_csv):
95
  list_output_NEOSP_SVR.append(float(linha[0]))
96
+ list_output_TFIDF_SVR = []
97
+ with open("metricas_{}_TFIDF.csv".format(nome_projeto), "r") as arquivo:
98
+ arquivo_csv = csv.reader(arquivo)
99
+ for i, linha in enumerate(arquivo_csv):
100
+ list_output_TFIDF_SVR.append(float(linha[0]))
101
+
102
+ return grafico(list_output_MbR, list_output_NEOSP_SVR, list_output_TFIDF_SVR, nome_projeto, pip_choices)
103
 
104
 
105
  demo = gr.Blocks()
 
111
  libraries = gr.Dropdown(choices=LIBRARIES, label="Projeto", value="ALOY")
112
  with gr.Column():
113
  gr.Markdown("## Gráficos")
114
+ pip = gr.CheckboxGroup(choices=["MbR Regressor", "NEOSP-SVR Regressor", "TFIDF-SVR Regressor"], label="Modelos Preditivos")
115
  # stars = gr.CheckboxGroup(choices=["Stars", "Week over Week"], label="")
116
  # issues = gr.CheckboxGroup(choices=["Issue", "Exclude org members", "week over week"], label="")
117
  with gr.Row():
 
119
  with gr.Row():
120
  with gr.Column():
121
  star_plot = gr.Text(visible=False, label="Wilcoxon Test")
122
+ star_plot2 = gr.Text(visible=False, label="Wilcoxon Test")
123
+ star_plot3 = gr.Text(visible=False, label="Wilcoxon Test")
124
  pip_plot = gr.Plot(visible=False)
125
 
126
  # issue_plot = gr.Plot(visible=False)
127
 
128
+ fetch.click(create_pip_plot, inputs=[libraries, pip], outputs=[pip_plot, star_plot, star_plot2, star_plot3])
129
  #fetch.click(create_star_plot, inputs=[libraries, pip], outputs=star_plot)
130
  # fetch.click(create_issue_plot, inputs=[libraries, issues], outputs=issue_plot)
131
 
database/tawos/README.md ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Tawosi Dataset
2
+
3
+ This directory consists 46 files, two files per each of the 26-3\*=23 projects:
4
+ - 23 files with "\_deep-se" suffix are prepared to be used by Deep-SE.
5
+ - 23 files with "\_tfidf-se" suffix are prepared to be used by TF/IDF-SE.
6
+
7
+ \* <sub>One of the repositories including three projects has been removed from the public domain during the time that the manuscript for this study [1] was under revision. Therefore, although the paper reports the results for all 26 projects, the replication package includes 23 projects as we refrain from publishing the data for the three remaining projects in accordance with The General Data Protection Regulation.</sub>
8
+
9
+ These 23 files are collected from 12 open source repositories by Tawosi et al. up until August, 2020.
10
+ The files named after their project key as "[project key]\_[approach].csv" e.g. MESOS_deep-se.csv, which is the set of issues collected from Appache repository Mesos project, and contains the features that Deep-SE needs for prediction. The following table shows the list of projects and the repositories where the project was collected from.
11
+
12
+ ## Project list
13
+
14
+ | Repository | Project | Key | File for Deep-SE | File for TF/IDF-SE |
15
+ |--------------|-----------------------------------|------------|---------------------------|---------------------------|
16
+ | Apache | Mesos | MESOS | MESOS_deeep-se.csv | MESOS_tfidf-se.csv |
17
+ | Apache | Alloy | ALOY | ALOY_deeep-se.csv | ALOY_tfidf-se.csv |
18
+ | Appcelerator | Appcelerator studio | TISTUD | TISTUD_deeep-se.csv | TISTUD_tfidf-se.csv |
19
+ | Appcelerator | Aptana studio | APSTUD | APSTUD_deeep-se.csv | APSTUD_tfidf-se.csv |
20
+ | Appcelerator | Command-Line Interface | CLI | CLI_deeep-se.csv | CLI_tfidf-se.csv |
21
+ | Appcelerator | Daemon | DAEMON | DAEMON_deeep-se.csv | DAEMON_tfidf-se.csv |
22
+ | Appcelerator | Documentation | TIDOC | TIDOC_deeep-se.csv | TIDOC_tfidf-se.csv |
23
+ | Appcelerator | Titanium | TIMOB | TIMOB_deeep-se.csv | TIMOB_tfidf-se.csv |
24
+ | Atlassian | Clover | CLOV | CLOV_deeep-se.csv | CLOV_tfidf-se.csv |
25
+ | Atlassian | Confluence Cloud | CONFCLOUD | CONFCLOUD_deeep-se.csv | CONFCLOUD_tfidf-se.csv |
26
+ | Atlassian | Confluence Server and Data Center | CONFSERVER | CONFSERVER_deeep-se.csv | CONFSERVER_tfidf-se.csv |
27
+ | DNNSoftware | DNN | DNN | DNN_deeep-se.csv | DNN_tfidf-se.csv |
28
+ | Duraspace | Duracloud | DURACLOUD | DURACLOUD_deeep-se.csv | DURACLOUD_tfidf-se.csv |
29
+ | Hyperledger | Fabric | FAB | FAB_deeep-se.csv | FAB_tfidf-se.csv |
30
+ | Hyperledger | Sawtooth | STL | STL_deeep-se.csv | STL_tfidf-se.csv |
31
+ | Lsstcorp | Data management | DM | DM_deeep-se.csv | DM_tfidf-se.csv |
32
+ | MongoDB | Compass | COMPASS | COMPASS_deeep-se.csv | COMPASS_tfidf-se.csv |
33
+ | MongoDB | Core Server | SERVER | SERVER_deeep-se.csv | SERVER_tfidf-se.csv |
34
+ | MongoDB | Evergreen | EVG | EVG_deeep-se.csv | EVG_tfidf-se.csv |
35
+ | Moodle | Moodle | MDL | MDL_deeep-se.csv | MDL_tfidf-se.csv |
36
+ | Mulesoft | Mule | MULE | MULE_deeep-se.csv | MULE_tfidf-se.csv |
37
+ | Sonatype | Sonatype’s Nexus | NEXUS | NEXUS_deeep-se.csv | NEXUS_tfidf-se.csv |
38
+ | Spring | Spring XD | XD | XD_deeep-se.csv | XD_tfidf-se.csv |
39
+
40
+ ## Content of the files
41
+
42
+ - Each csv file for Deep-SE approach contains 4 columns: *issuekey*, *created*, *title*, *description*, and *storypoint*.
43
+
44
+ - Each csv file for TF/IDF-SE approach contains more than 4 columns: starting with *issuekey*, *created*, *storypoint*, *context*, *codesnippet*, and a set of one-hot columns for issue type (header starting with t\_) followed by component(s) (header starting with c\_).
45
+
46
+ - The issues are sorted based on issue's creation time (i.e. the former issues was created before the latter issues).
47
+
48
+ [1] Vali Tawosi, Rebecca Moussa, and Federica Sarro. "Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study." IEEE Transactions on Software Engineering, no. TBA (2022): pp. TBA.
database/tawos/deep/ALOY_deep-se.csv ADDED
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database/tawos/deep/APSTUD_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/CLI_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/CLOV_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/COMPASS_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/CONFCLOUD_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/CONFSERVER_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/DAEMON_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/DM_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/DNN_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/DURACLOUD_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/EVG_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/FAB_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/MDL_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/MESOS_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/MULE_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/NEXUS_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/SERVER_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/STL_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/TIDOC_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/TIMOB_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/TISTUD_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/XD_deep-se.csv ADDED
The diff for this file is too large to render. See raw diff
 
database/tawos/deep/metricas_NEXUS_MbR.csv ADDED
@@ -0,0 +1,300 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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