Automated bug report field reassignment and refinement prediction

Date of Publication (online): 26 October 2015</p

EnHMM: On the Use of Ensemble HMMs and Stack Traces to Predict the Reassignment of Bug Report Fields

Bug reports (BR) contain vital information that can help triaging teams prioritize and assign bugs to developers who will provide the fixes. However, studies have shown that BR fields often contain incorrect information that need to be reassigned, which delays the bug fixing process. There exist approaches for predicting whether a BR field should be reassigned or not. These studies use mainly BR descriptions and traditional machine learning algorithms (SVM, KNN, etc.). As such, they do not fully benefit from the sequential order of information in BR data, such as function call sequences in BR stack traces, which may be valuable for improving the prediction accuracy. In this paper, we propose a novel approach, called EnHMM, for predicting the reassignment of BR fields using ensemble Hidden Markov Models (HMMs), trained on stack traces. EnHMM leverages the natural ability of HMMs to represent sequential data to model the temporal order of function calls in BR stack traces. When applied to Eclipse and Gnome BR repositories, EnHMM achieves an average precision, recall, and F-measure of 54%, 76%, and 60% on Eclipse dataset and 41%, 69%, and 51% on Gnome dataset. We also found that EnHMM improves over the best single HMM by 36% for Eclipse and 76% for Gnome. Finally, when comparing EnHMM to Im.ML.KNN, a recent approach in the field, we found that the average F-measure score of EnHMM improves the average F-measure of Im.ML.KNN by 6.80% and improves the average recall of Im.ML.KNN by 36.09%. However, the average precision of EnHMM is lower than that of Im.ML.KNN (53.93% as opposed to 56.71%).Comment: Published in Proceedings of the 28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER 2021), 11 pages, 7 figure

Automatic bug triaging techniques using machine learning and stack traces

When a software system crashes, users have the option to report the crash using automated bug tracking systems. These tools capture software crash and failure data (e.g., stack traces, memory dumps, etc.) from end-users. These data are sent in the form of bug (crash) reports to the software development teams to uncover the causes of the crash and provide adequate fixes. The reports are first assessed (usually in a semi-automatic way) by a group of software analysts, known as triagers. Triagers assign priority to the bugs and redirect them to the software development teams in order to provide fixes. The triaging process, however, is usually very challenging. The problem is that many of these reports are caused by similar faults. Studies have shown that one way to improve the bug triaging process is to detect automatically duplicate (or similar) reports. This way, triagers would not need to spend time on reports caused by faults that have already been handled. Another issue is related to the prioritization of bug reports. Triagers often rely on the information provided by the customers (the report submitters) to prioritize bug reports. However, this task can be quite tedious and requires tool support. Next, triagers route the bug report to the responsible development team based on the subsystem, which caused the crash. Since having knowledge of all the subsystems of an ever-evolving industrial system is impractical, having a tool to automatically identify defective subsystems can significantly reduce the manual bug triaging effort. The main goal of this research is to investigate techniques and tools to help triagers process bug reports. We start by studying the effect of the presence of stack traces in analyzing bug reports. Next, we present a framework to help triagers in each step of the bug triaging process. We propose a new and scalable method to automatically detect duplicate bug reports using stack traces and bug report categorical features. We then propose a novel approach for predicting bug severity using stack traces and categorical features, and finally, we discuss a new method for predicting faulty product and component fields of bug reports. We evaluate the effectiveness of our techniques using bug reports from two large open-source systems. Our results show that stack traces and machine learning methods can be used to automate the bug triaging process, and hence increase the productivity of bug triagers, while reducing costs and efforts associated with manual triaging of bug reports

Enhanced Approach for Bug Severity Prediction: Experimentation and Scope for Improvements

Software development is an iterative process, where developers create, test, and refine their code until it is ready for release. Along the way, bugs and issues are inevitable. A bug can be any error identified in requirement specification, design or implementation of any project. These bugs need to be categorized and assigned to developers to be resolved. the number of bugs generated in any large scale project are vast in number. These bugs can have significant or no impact on the project depending on the type of bug. The aim of this study is to develop a deep learning-based bug severity prediction model that can accurately predict the severity levels of software bugs. This study aims to address the limitations of the current manual bug severity assessment process and provide an automated solution using various classifiers e.g. Naïve Bayes, Logistic regression, KNN and Support vector machine along with Mutual information as feature selection method, that can assist software development teams in giving severity code to bugs effectively. It seeks to improve the overall software development process by reducing the time and effort required for bug resolution and enhancing the quality and reliability of software

High impact bug report identification with imbalanced learning strategies

Supplementary code and data available from GitHub: https://github.com/goddding/JCST</p

An Industrial Study on Predicting Crash Report Log Types Using Large Language Models

Software crashes and failures take a fair amount of effort and time to resolve. Software developers use information submitted in crash reports (CRs) to conduct root cause analysis of faults. The problem is that CRs often lack all the information required. Automatic prediction of CR fields can therefore reduce the crash resolution process time. In this thesis, we use CR headings and descriptions to predict the type of log files that should be attached to a CR. Our approach is to use multilabel learning algorithms to train a machine learning model using a dataset from Ericsson’s CR database to predict the type of log files based on CR headings and descriptions. We use three different pre-trained language models Bert, Telecom Bert, and Word2Vector to extract feature vectors from CR headings and descriptions and then feed these vectors to three different multilabel learning algorithms, namely Binary Relevance (BR), Classifier Chain (CC), and Neural Network (NN). Then, we compare the performance of different feature sets. We found that the use of headings alone with pre-trained language models Bert and Telecom Bert results in the best average AUC (0.70). The use of descriptions and headings and descriptions together as features resulted in an average AUC varying from 0.65 to 0.70. In general, the algorithms showed no significant difference in their performances, but the choice of features impacts the performance. Also, the performance of predicting each type of log is influenced by the use of keywords in headings and descriptions that describe these files. We found that log types with a clear definition such as Key Performance Indicators (KPI) Logs, Post-mortem Dumps (PMD), and execution traces can be predicted with higher accuracy

On the Use of Software Tracing and Boolean Combination of Ensemble Classifiers to Support Software Reliability and Security Tasks

In this thesis, we propose an approach that relies on Boolean combination of multiple one-class classification methods based on Hidden Markov Models (HMMs), which are pruned using weighted Kappa coefficient to select and combine accurate and diverse classifiers. Our approach, called WPIBC (Weighted Pruning Iterative Boolean Combination) works in three phases. The first phase selects a subset of the available base diverse soft classifiers by pruning all the redundant soft classifiers based on a weighted version of Cohen’s kappa measure of agreement. The second phase selects a subset of diverse and accurate crisp classifiers from the base soft classifiers (selected in Phase1) based on the unweighted kappa measure. The selected complementary crisp classifiers are then combined in the final phase using Boolean combinations. We apply the proposed approach to two important problems in software security and reliability: The detection of system anomalies and the prediction of the reassignment of bug report fields. Detecting system anomalies at run-time is a critical component of system reliability and security. Studies in this area focus mainly on the effectiveness of the proposed approaches -the ability to detect anomalies with high accuracy. Less attention was given to false alarm and efficiency. Although ensemble approaches for the detection of anomalies that use Boolean combination of classifier decisions have been shown to be useful in reducing the false alarm rate over that of a single classifier, existing methods rely on an exponential number of combinations making them impractical even for a small number of classifiers. Our approach is not only able to maintain and even improve the accuracy of existing Boolean combination techniques, but also significantly reduce the combination time and the number of classifiers selected for combination. The second application domain of our approach is the prediction of the reassignment of bug report fields. Bug reports contain a wealth of information that is used by triaging and development teams to understand the causes of bugs in order to provide fixes. The problem is that, for various reasons, it is common to have bug reports with missing or incorrect information, hindering the bug resolution process. To address this problem. researchers have turned to machine learning techniques. The common practice is to build models that leverage historical bug reports to automatically predict when a given bug report field should be reassigned. Existing approaches have mainly relied upon classifiers that make use of natural language in the title and description of the bug reports. They fail to take advantage of the richly detailed sequential information that is present in stack traces included in bug reports. To address this, we propose an approach called EnHMM which uses WPIBC and stack traces to predict the reassignment of bug report fields. Another contribution of this thesis is an approach to improve the efficiency of WPIBC by leveraging the Hadoop framework and the MapReduce programming model. We also show how WPIBC can be extended to support heterogenous classifiers