Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy

Algorithm 1

OIILS algorithm.

	Require: Training data (the data after balancing), the probability and of each bug, the constraint solving model .
	Ensure: The category of each bug.
(1)	= 0; //Initialization
(2)	= 0.5,  = 0.5; //Initializes the weights.
(3)	/Weight training phase./
(4)	for each
(5)	if belongs to minority class
(6)	= −1;
(7)	else
(8)	= 1;
(9)	end if
(10)	end for
(11)
(12)	/Let the objective function be maximizing the highest achievable accuracy of the classifier./
(13)	/Generate all the CONSTRAINTS./
(14)	CONSTRAINTS ;
(15)	CONSTRAINTS ;
(16)	CONSTRAINTS ;
(17)	/The formulation is built successfully./
(18)	for each classifier
(19)	; // Obtain the most suitable weights by optimization.
(20)	end for
(21)	/Weight adjustment phase./
(22)	for each do
(23)	; // is the original majority probability of .
(24)	; // is the original majority probability of .
(25)	end for
(26)	/Minimum selection/
(27)	for each do
(28)	minimum value of majority class probabilities;
(29)	minimum value of majority class probabilities;
(30)	if
(31)	Category of majority class;
(32)	else
(33)	Category of minority class;
(34)	end if
(35)	end for
(36)	return the category of each bug report