Software Defect Fault Intelligent Location and Identification Method Based on Data Mining

With the advancement of the times, computer technology is also constantly improving, and people’s requirements for software functions are also constantly improving, and as software functions become more and more complex, developers are technically limited and teamwork is not tacitly coordinated. And so on, so in the software development process, some errors and problems will inevitably lead to software defects. The purpose of this paper is to study the intelligent location and identification methods of software defects based on data mining. This article first studies the domestic and foreign software defect fault intelligent location technology, analyzes the shortcomings of traditional software defect detection and fault detection, then introduces data mining technology in detail, and finally conducts in-depth research on software defect prediction technology. Through in-depth research on several technologies, it reduces the accidents of software equipment and delays its service life. According to the experiments in this article, the software defect location proposed in this article uses two methods to compare. The first error set is used as a unit to measure the subsequent error set software error location cost. The first error set 1F contains 19 A manually injected error program, and the average positioning cost obtained is 3.75%.

Privacy preserving defect prediction using generalization and entropy-based data reduction

The software engineering community produces data that can be analyzed to enhance the quality of future software products, and data regarding software defects can be used by data scientists to create defect predictors. However, sharing such data raises privacy concerns, since sensitive software features are usually considered as business assets that should be protected in accordance with the law. Early research efforts on protecting the privacy of software data found that applying conventional data anonymization to mask sensitive attributes of software features degrades the quality of the shared data. In addition, data produced by such approaches is not immune to attacks such as inference and background knowledge attacks. This research proposes a new approach to share protected release of software defects data that can still be used in data science algorithms. We created a generalization (clustering)-based approach to anonymize sensitive software attributes. Tomek link and AllNN data reduction approaches were used to discard noisy records that may affect the usefulness of the shared data. The proposed approach considers diversity of sensitive attributes as an important factor to avoid inference and background knowledge attacks on the anonymized data, therefore data discarded is removed from both defective and non-defective records. We conducted experiments conducted on several benchmark software defect datasets, using both data quality and privacy measures to evaluate the proposed approach. Our findings showed that the proposed approach outperforms existing well-known techniques using accuracy and privacy measures.

Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

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.

On Predicting Rediscoveries of Software Defects

The same defect may be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. One solution is forcing clients to install a fix for every defect. However, this approach is economically infeasible, because it requires extra resources and increases downtime. Moreover, it may lead to regression of functionality, as new fixes may break the existing functionality. Our goal is to find a way to proactively predict defects that a client may rediscover in the future. We build a predictive model by leveraging recommender algorithms. We evaluate our approach with extracted rediscovery data from four groups of large-scale open source software projects (namely, Eclipse, Gentoo, KDE, and Libre) and one enterprise software. The datasets contain information about ⇡ 1.33 million unique defect reports over a period of 18 years (1999-2017). Our proposed approach may help in understanding the defect rediscovery phenomenon, leading to improvement of software quality and customer satisfaction.

On Predicting Rediscoveries of Software Defects

The same defect may be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. One solution is forcing clients to install a fix for every defect. However, this approach is economically infeasible, because it requires extra resources and increases downtime. Moreover, it may lead to regression of functionality, as new fixes may break the existing functionality. Our goal is to find a way to proactively predict defects that a client may rediscover in the future. We build a predictive model by leveraging recommender algorithms. We evaluate our approach with extracted rediscovery data from four groups of large-scale open source software projects (namely, Eclipse, Gentoo, KDE, and Libre) and one enterprise software. The datasets contain information about ⇡ 1.33 million unique defect reports over a period of 18 years (1999-2017). Our proposed approach may help in understanding the defect rediscovery phenomenon, leading to improvement of software quality and customer satisfaction.

Intelligent Radar Software Defect Classification Approach based on the Latent Dirichlet Allocation Topic Model

Existing software intelligent defect classification approaches don’t 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, 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 defecs effectively to improve the identifying adequacy of the defects in radar software.