Abstract: Aiming at the the question of dimension disaster in software reliability prediction，a new method of software metrics selection based on Adaptive Genetic Algorithm（AGA） and K-Nearest Neighbor（KNN） algorithm is put forward.It is used to filter a pivotal metrics subset which is super correlative with software reliability.It uses genetic algorithm to search the metrics subset randomly and uses the classification error rate of KNN classifier as the learning algorithm and evaluation method.The simulation results show the dimension of the pivotal attribute selected by AGA-KNN is obviously smaller than the original metrics set，and the pivotal attribute subset can make the classification accuracy higher than used all the metrics data.

Key words: software reliability prediction, software metrics, attribute selection, genetic algorithm, K-Nearest Neighbor（KNN）

摘要： 针对软件可靠性预测中软件度量维数灾难问题，提出一种基于自适应遗传算法和KNN算法相结合的软件度量属性选择方法，筛选出与软件可靠性关系最为密切的关键属性集。该方法在属性子集搜索上采用遗传算法进行随机搜索，在属性子集评价上采用KNN分类准确率和属性子集规模作为学习算法及评价指标。实验结果表明，该算法可有效地找出具有较好可分离性的属性子集，从而实现降维并提高软件可靠性预测精度。

关键词: 软件可靠性预测, 软件度量, 属性选择, 遗传算法, KNN算法