Hybrid Optimization of Multiple Intelligent Recommendation Engines for Software Development Cycles

The primary purpose is to create a hybrid recommendation system approach to improve the performance of such systems. This recommendation system would typically be used to assign or suggest a small number of developers suitable for troubleshooting a bug report. For example, managing collections inside bug repositories is software developers' task to fix any bugs that have been identified. Unfortunately, bugs are often created, so the number of developers needed is high, so it's hard to decide how many to assign to specific tasks.This better aims better to understand the outcomes of the latestscientific methods. We also addressed developer prioritization and how it can be used to determine the assignment of a problem to a developer. We have studied two aspects: first, selecting bug reports using hybrid machine learning methods, modeling prioritization in the bug repository, and supporting developer assignment tasks with our model. Second, we modeled the relevant objectives suggested by the developers' backgrounds based on proven knowledge and experience. The study focuses on two topers' experience with fixing bugs and developer rankings in the App Store. We've tried to take better assignments using developer prioritization in bug repositories, e.g., bug triage, severity identification, and re-opened bug prediction. We examine the output of the model in a representative sample of bug repositories. The results show that the prioritization of developers' prioritization triage worker and allow the program to solve the bugs more effectively in support of the software support has been clarified. The introduction article, section 2 on the literature and context, section 3 on the work contribution that will be made, section 4 on the methodology analysis and the expected outcomes will be explained, section 5 on the conclusion, and finally, on the potentialaspects of this work

High-Dimensional Hybrid Data Reduction for Effective Bug Triage

Owing to the ever-expanding scale of software, solving the problem of bug triage efficiently and reasonably has become one of the most important issues in software project maintenance. However, there are two challenges in bug triage: low quality of bug reports and engagement of developers. Most of the existing bug triage solutions are based on the text information and have no consideration of developer engagement, which leads to the loss of bug triage accuracy. To overcome these two challenges, we propose a high-dimensional hybrid data reduction method that combines feature selection with instance selection to build a small-scale and high-quality dataset of bug reports by removing redundant or noninformative bug reports and words. In addition, we also study the recent engagement of developers, which can effectively distinguish similar bug reports and provide a more suitable list of the recommended developers. Finally, we experiment with four bug repositories: GCC, OpenOffice, Mozilla, and NetBeans. We experimentally verify that our method can effectively improve the efficiency of bug triage.

Multi-Feature Approach for Bug Severity Assignment

When bug reports are submitted through bug tracking systems, they are analysed manually to identify their severity levels. A severity level specifies the negative impact of a bug on a system. With the huge number of submitted reports, setting the severity class manually is tedious and time consuming. Moreover, some bug types are reported more often than other types, which leads to imbalanced bug repositories. This paper proposes a multi-feature approach for automatic severity assignment, which leverages lexical, semantic, and categorical properties of the bug reports. The proposed approach utilizes word embeddings, topic model, vector space model, and an adapted K-Nearest Neighbour technique. Moreover, the impact of utilizing two sampling techniques, namely SMOTE and cluster-based under-sampling (CBU), were investigated. Experiments over two open source repositories, Eclipse and Mozilla, demonstrated that the proposed approach is superior to two previous studies.

Surprise Bug Report Prediction Utilizing Optimized Integration with Imbalanced Learning Strategy

In software projects, a large number of bugs are usually reported to bug repositories. Due to the limited budge and work force, the developers often may not have enough time and ability to inspect all the reported bugs, and thus they often focus on inspecting and repairing the highly impacting bugs. Among the high-impact bugs, surprise bugs are reported to be a fatal threat to the software systems, though they only account for a small proportion. Therefore, the identification of surprise bugs becomes an important work in practices. In recent years, some methods have been proposed by the researchers to identify surprise bugs. Unfortunately, the performance of these methods in identifying surprise bugs is still not satisfied for the software projects. The main reason is that surprise bugs only occupy a small percentage of all the bugs, and it is difficult to identify these surprise bugs from the imbalanced distribution. In order to overcome the imbalanced category distribution of the bugs, a method based on machine learning to predict surprise bugs is presented in this paper. This method takes into account the textual features of the bug reports and employs an imbalanced learning strategy to balance the datasets of the bug reports. Then these datasets after balancing are used to train three selected classifiers which are built by three different classification algorithms and predict the datasets with unknown type. In particular, an ensemble method named optimization integration is proposed to generate a unique and best result, according to the results produced by the three classifiers. This ensemble method is able to adjust the ability of the classifier to detect different categories based on the characteristics of different projects and integrate the advantages of three classifiers. The experiments performed on the datasets from 4 software projects show that this method performs better than the previous methods in terms of detecting surprise bugs.

Combining Imbalance Learning Strategy and Multiclassifier Estimator for Bug Report Classification

Since a large number of bug reports are submitted to the bug repository every day, efficiently assigning bug reports to the correct developer is a considerable challenge. Because of the large differences between the different components of different projects, the current bug classification mainly relies on the components of the bug report to dispatch bug reports to the designated developer or developer community. Unfortunately, the component information of the bug report is filled in by default according to the bug submitter and the result is often incorrect. Thus, an automatic technology that can identify high-impact bug reports can help developers to be aware of them early, rectify them quickly, and minimize the damages they cause. In this paper, we propose a method based on the combination of imbalanced learning strategies such as random undersampling (RUS), random oversampling (ROS), synthetic minority oversampling technique (SMOTE), and AdaCost algorithms with multiclass classification methods, OVO and OVA, to solve bug reports component classification problem. We investigate the effectiveness of different combinations, i.e., variants, each of which includes a specific imbalance learning strategy and a specific classification algorithm. We mainly perform an analytical study on five open bug repositories (Eclipse, Mozilla, GCC, OpenOffice, and NetBeans). The results show that different variants have different performance for bug reports component identification and the best performance variants are combined with the imbalanced learning strategy RUS and the OVA method based on the SVM classifier.