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基于机器学习和LDA主题模型的缺陷报告分派方法的Python实现。原论文为:Accurate developer recommendation for bug resolution.

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DevRec:Accurate developer recommendation for bug resolution


该项目实现了一种基于多标签机器学习和LDA主题概率模型的软件缺陷报告分派方法。

代码纯粹本人手写,初次使用python和机器学习在严肃话题,代码注释不够或者混乱还请见谅。


代码基本复现了DevRec报告分派方法,但仍缺少降噪处理,在小数据集验证下,性能比原文差一些。 根据原文的验证方法,将数据集11等分,训练第一个数据集,并用地二个测试,接下来训练第一第二个数据集,用第三个预测,以此类推。

+ [OK] read file!
+ [OK] the file has be Standardized!
+ [OK] dictionary is saved in dictionary !
num_topics = 21

+ [OK] LDA model is saved!
+ [OK] LDA model is ready!

test only br-analysis
r5: 1.0
r10: 1.0

r5: 1.0
r10: 1.0

r5: 0.782051282051282
r10: 1.0

r5: 0.7458333333333333
r10: 1.0

r5: 0.7125000000000001
r10: 1.0

r5: 0.8729166666666668
r10: 1.0

r5: 0.8159722222222223
r10: 1.0

r5: 0.811437908496732
r10: 0.9683006535947711

r5: 0.6088050314465409
r10: 0.7556603773584903

r5: 0.6372549019607843
r10: 1.0

 + [OK]find gama:
 [0.7238926662173903, 0.522715297056122, 0.02707073571840457, 0.23472140478470704, 0.27757847785072043]
 [0.47339674445300617, 0.9199228917603774, 0.19994059851075652, 0.3000426906721848, 0.22600152836999565]

 + [OK] test --- !
 r5: 0
 r10: 0

 r5: 1.0
 r10: 1.0

 r5: 1.0
 r10: 1.0

 r5: 1.0
 r10: 1.0

 r5: 1.0
 r10: 1.0

 r5: 1.0
 r10: 1.0

 r5: 0.2465277777777778
 r10: 0.19791666666666666

 r5: 0.24542483660130718
 r10: 0.30163398692810456

 r5: 0.40220125786163524
 r10: 0.5009433962264151

 r5: 0.6029411764705882
 r10: 0.8725490196078431

原论文所提供的java代码在本仓库内也有提供。

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基于机器学习和LDA主题模型的缺陷报告分派方法的Python实现。原论文为:Accurate developer recommendation for bug resolution.

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