# Graph-Driven Exploration of Issue Handling Schemes in Software Projects
## 2 Literature Review and Problem Statement  
Software repositories are used in numerous studies on diverse software engineering aspects such as the classification of issues, bug localization (diagnostics), task allocation, issue lifecycle tracing, and predicting specified issue or project features. Reported issues have different levels of importance specified by priority or severity attributes. They can be fixed by issue reporters, modified by other project actors, or derived from issue descriptions. In [15], a two-stage approach is proposed to predict the goal of opening an issue and its priority. This approach uses feature engineering methods and text classifiers. A survey on issue severity predictions based on issue descriptions is given in [16], confirming the usefulness of text mining (machine learning) supported by unstructured text features. Security-related issues need special attention due to their criticality. Security bug reports are predicted based on constructed knowledge graphs and finding security-related words in bug reports [17]. Issues reporting security vulnerabilities can be identified by checking for the presence of security-related keywords with appropriate filtering [18] to avoid mislabeling. Issue importance (criticality) impacts the order of issue resolution, so its correct specification is compelling. ITS repositories comprise diverse issue types, e.g., bugs, new functionalities, performance improvements, code merging, and system reconfiguration, and thus require need appropriate handling services. In practice, reported issues may not be labelled or may be labelled incorrectly. Hence, automatic classification is still helpful in issue management, and it uses text mining techniques applied to the issue title and description. The fundamental classification discriminates between bug and non-bug issues [6]. Issues of the bug type with a higher priority should be resolved prior to the release of the code. Some issues do not need fixing (will not fix issues [19]) due to the ability to postpone for another code version or having an acceptably negligible impact. In complex projects involving many actors, a significant percentage of duplicate issue reports may appear. Their identification can significantly reduce redundant activities and avoid wasting developer resources. There exist a wide variety of approaches to automatically detect duplicate bug reports, such as text mining their descriptions and using similarity measures and some other statistics [5]. The precision of detecting duplicate bug reports can be improved by converting unstructured textual descriptions into structural data [4]. Numerous papers concentrate on issue classifications related to specified categories. The neural network RoBERT [20] identifies three issue categories: bug, enhancement, and question. [PERSON] et al. [21] developed a prediction model to identify valid, invalid, and performance- and aging-related bug reports. The best results were achieved with the Support Vector Machine (SVM) classifier. In our previous paper [22] (and included references), we discussed some other issue classification schemes. The ITS facilitates characterizing issue types with default or customized labels. This can be enhanced by extracting issue information (report descriptions and comments) and applying text mining classifiers [23]. Correlating ITS and VCS repositories, bug classification can reflect the scope of code and the number of needed fixes [23]. Bug type identification is crucial in bug triage, localization, and fixing processes. [PERSON] and [PERSON] [24] proposed a robust and effective classification model targeting these aspects. A unified model for bug classification and assignment problems is presented in [7]. It organizes data in a knowledge graph. Problem localization is important in issue handling. Investigating textual similarities of bug descriptions and source code files helps in localizing bugs [25]. Here, we can also use machine learning techniques.