id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_w73hiptz3bcxri5xhr47jx335e	Rainer Niedermayr	Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk	2019	28	.pdf	application/pdf	13619	1586	64	resources, development teams can apply defect prediction to identify fault-prone Method: We compute code metrics and apply association rule mining to create rules trained a classifier for methods with LFR using association rule mining. the metrics for each method, the data pre-processing, and the association rule mining Defect prediction models use code metrics (Menzies, comprises the computation of source-code metrics for each method, the data preprocessing before the mining, and the association rule mining. Like defect prediction models, IDP uses metrics to train a classifier for identifying LFR traditional defect prediction approaches are binary classifications, which classify a method precision than to predict all methods that do not contain any faults in the dataset. Table 5 RQ 1, RQ 2: Evaluation of within-project IDP to identify low-fault-risk (LFR) methods. An inverse view on defect prediction to identify methods with low fault risk An inverse view on defect prediction to identify methods with low fault risk	./cache/work_w73hiptz3bcxri5xhr47jx335e.pdf	./txt/work_w73hiptz3bcxri5xhr47jx335e.txt