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1 Commit Guru 1 [1] (commit) Yang [2] Wang [3] Sharma [4] [5] (CNN:Convolutional Neural Networks) ( ) 1 Commit Guru: 130 SEA

2 [5] [6] [7 10] 2 [5] (RQ:Research Question) RQ1:? RQ2:? Commit Guru Commit Guru [1] Emad Shihab Web Commit Guru [10] Number of Subsystems(NS: ) Number of Directories(ND: ) Number of Files(NF: ) Entropy( ) Line Added(LA: ) Line Deleted(LD: ) Lines Total(LT: ) Number Developers(NDEV: ) Age of changes (AGE: ) Number of unique changes(nuc: ) Developer Experience(EXP: ( commit )) Recent Experience(REXP: ( commit )) Experience on a subsystem(sexp: ( commit )) 13 Commit Guru commit 131 SEA

3 [11] bug fix wrong error fail problem patch commit bug=1 bug=0 Commit Guru 3.2 Keras Keras 2 Python( ) TensorFlow CNTK Theano Keras (RNN: Reccurent Neural Networks) CPU GPU Keras 3.3 TensorFlow TensorFlow 3 Google TensorFlow Python 2 Keras: 3 TensorFlow: CPU GPU TensorFlow CPU GPU 3.2 Keras TensorFlow Commit Guru Commit Guru Git bitcoin bitcoin 1 C++ bitcoin commit Git commit Commit Guru Commit Guru commit. 4.2 git diff commit git diff 1 - commit + commit git diff RGB NotoMono-Regular 132 SEA

4 Commit Guru 1. git diff Google 8 2 RGB Commit Guru Commit Guru 1 ( ) w w w w 3 1 w (overfitting) ( ) Dropout Dropout 133 SEA

5 3. ( ) 4.2 RGB 3 1/3 Red 1/3 Green 1/3 Blue % Dropout ReLU ( 1 4) 2 softmax ( 2 5) Relu(x) = so f tmax(x i ) = x (x 0) 0 (x < 0)ring (1) e x i nj=1 e x j (i = 1, 2,, n) (2) ReLU SEA

6 1. bug non-bug bug TP FN non-bug FP TN softmax (Confusion Matrix) 1 ( 2 ) ( ). (TP:True Positive): (bug) (bug) (FP:False Positive): (bug) (non-bug) (FN:False Negative): (non-bug) (bug) ReLU % Dropout 2 softmax (TN:True Negative): (non-bug) (non-bug) Recall( ) Precision( ) F1-score(F1 ) Accuracy( ) Recall( ) Recall( ) (bug) ( 1). Recall = T P T P + FN (3) 135 SEA

7 4.5.2 Precision( ) Precision( ) ( 1) Precision = F1-score(F1 ) T P T P + FP (4) F1-score(F1 ) recall( ) Precision( ) F1 = accuracy( ) 2 Rcall Precision Recall + Precision (5) accuracy( ) ( 1) 2. nunber of files train-bug 2,071 train-nonbug 11,984 test-bug 208 test-nonbug 1, (train loss) Accuracy = T P + T N T P + FP + FN + T N (6) (RQ) 5.1 RQ1:? (train loss) RQ2:? RQ1 136 SEA

8 7. (train loss) 8. (train loss) RQ Accuracy( ) Recall( ) Precision( ) F1-score(F1 ) ( ) 4 (208 ) 18 Accuracy( ) RQ1 9 (train loss) (test loss) (train loss) (test loss) 2. Commit Guru commit 137 SEA

9 3. Recall Precision F1-score Accuracy bug non-bug bug non-bug Dropout 6 (5.1.2 ) ( ) 138 SEA

10 7 [1] C. Rosen, B. Grawi, and E. Shihab, Commit guru: Analytics and risk prediction of software commits, Proceedings of the th Joint Meeting on Foundations of Software Engineering, pp , New York, NY, USA, July [2] X. Yang, D. Lo, X. Xia, Y. Zhang, and J. Sun, Deep learning for just-in-time defect prediction, QRS 15: Proceeding of the 2015 IEEE International Conference on Software Quality, Reliability and Security, pp.17 26, Washington, DC, USA, Aug [3] S. Wang, T. Liu, and L. Tan, Automatically learning semantic features for defect prediction, ICSE 16: Proceedings of the 38th International Conference on Software Engineering, pp , New York, NY, USA, May [5] 2017 vol.2017 pp Aug [6] A. Hindle, M. Godfrey, and R. Holt, Reading beside the lines: Indentation as a proxy for complexity metrics, In Program Comprehension, ICPC The 16th IEEE International Conference on, pp , May [7] S. Kim, J.E.J. Whitehead, and Y. Zhang, Classifying software changes: Clean or buggy?, IEEE Transactions on Software Engineering, vol.34, no.2, pp , March [8] L. Aversano, L. Cerulo, and C.D. Grosso, Learning from bug-introducing changes to prevent fault prone code, IWPSE 07: Ninth international workshop on Principles of software evolution: in conjunction with the 6th ESEC/FSE joint meeting, pp.19 26, NY, USA, July [9] T. Jiang, L. Tan, and S. Kim, Personalized defect prediction, ASE 13: Proceedings of the 28th IEEE/ACM International Conference on Automated Software Engineering, pp , NJ, USA, Nov [10] Y. Kamei, E. Shihab, B. Adams, A.E. Hassan, A. Mockus, A. Sinha, and N. Ubayashi, A large-scale empirical study of just-in-time quality assurance, IEEE Transactions on Software Engineering, vol.39, no.6, pp , June [11] A. Hindle, D.M. German, and R. Holt, What do large commits tell us?: a taxonomical study of large commits, MSR 08: Proceedings of the 2008 international working conference on Mining software repositories, pp , New York, NY, USA, May [4] R. Sharma and P. Kakkar, Software module fault prediction using convolutional neural network with feature selection, International Journal of Software of Software Engineering and Its Applications, vol.10, no.12, pp , SEA

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