Abstract
Existing software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.
Highlights
In this paper, existing techniques such as the latent Dirichlet allocation (LDA) topic model and inverse maximum matching algorithm are required
We propose the modified LDA topic model incorporating the features of radar software requirements to obtain the potential topics of software requirement such as the function names and the interface names, to improve the accuracy of the LDA model topic identification; based on the LDA topic model, we realize radar software defect classification
The specific principle of the LDA topic model is shown in the following Fig. 1 [11]: In Fig. 1, assuming that the document corpus has D documents, there are N words in the corpus, Wd,n represents the nth word in the dth document, and each document consists of k topics Composition, the topicword probability distribution under each topic φk obeys the Dirichlet distribution with β as the parameter, θd is the document-topic distribution, each document corresponds to a different topic distribution, θd obeys the Dirichlet distribution withα as the parameter, Zd,n,n represents the specified distribution between topics and words within the defect data d, and Zd,n obeys the polynomial distribution with θz as the parameter
Summary
1.1 Related works At present, the research on the application of artificial intelligence techniques in the domain of software defect classification and prediction is divided into two main types as follows: 1.1.1 (1) Intelligent algorithm-based software testing usage [2–6] At present, the existing software testing knowledge reuse technologies based on the intelligent algorithm are mostly focused on the research of test case reuse, in another word, based on the historical test cases, with the help of an intelligent algorithm, and the reusable test cases are recommended for the current projects to improve software testing efficiency and reduce work costs. Based on the acquired topic models, the classification of software defect data can be achieved Among these topic models, the LDA topic model does not require additional annotation and processing of the training set, is unsupervised learning, has less technical difficulty and workload, and has been more widely studied and applied in text classification [10–12]. Based on the radar domain dictionary and the inverse maximum matching algorithm, the software defective text word separation algorithm implemented in this paper is shown in Fig. 3: Step 1: Assume that X1 is the radar software defect data string to be divided into words, the output string X2 is the empty set, and MaxWord is the maximum word length in the radar domain dictionary.
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