Bug Reports Research Articles

Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability

This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. The LSTM model is trained and evaluated on a dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The dataset's distribution of bug categories and statuses further provides valuable insights into common software development issues.

PCG: A joint framework of graph collaborative filtering for bug triaging

AbstractBug triaging is a vital process in software maintenance, involving assigning bug reports to developers in the issue tracking system. Current studies predominantly treat automatic bug triaging as a classification task, categorizing bug reports using developers as labels. However, this approach deviates from the essence of triaging, which is establishing bug–developer correlations. These correlations should be explicitly leveraged, offering a more comprehensive and promising paradigm. Our bug triaging model utilizes graph collaborative filtering (GCF), a method known for handling correlations. However, GCF encounters two challenges in bug triaging: data sparsity in bug fixing records and semantic deficiency in exploiting input data. To address them, we propose PCG, an innovative framework that integrates prototype augmentation and contrastive learning with GCF. With bug triaging modeled as predicting links on the bipartite graph of bug–developer correlations, we introduce prototype clustering‐based augmentation to mitigate data sparsity and devise a semantic contrastive learning task to overcome semantic deficiency. Extensive experiments against competitive baselines validate the superiority of PCG. This work may open new avenues for investigating correlations in bug triaging and related scenarios.

Bug reports priority classification models. Replication study

Bug reports priority classification models. Replication study

Automatic App Review Using Android Application

DeepMatcher comes with a tool that connects usage data from app analytics to error reports in the monitored data. DeepMatcher uses advanced techniques to automate this process, enabling developers to quickly spot bugs and dynamically improve bug reporting. In this Paper we have got Simple Mail Transfer protocol (SMTP) which can send and receive Mail to concerned app developers for the improvement in app experience. As our abstract aims to make a standalone app that can make the user give feedback directly to developer so that the app satisfies the user as NLP required in DeepMatcher to report the SMTP used is simplified and make the developer easily rectify the issue with regards to this research we propose DeepMatcher, a tool to automate bug detection and enhance bug reporting in app development .We contrast our solution to an alternative, SMTP (Simple Mail Transfer Protocol), featuring distinct advantages. Whilst DeepMatcher illustrates automation, SMTP provides user-to-developer direct and immediate communication channels, simplifying feedback submission and bug reports. This direct approach provides timely and detailed feedback, faster bug resolution and higher app quality resulting in improved user satisfaction. Keywords— SMTP, CoRe, Standalone, Feedback, Developer, Generated

Adaptive attention fusion network for cross-device GUI element re-identification in crowdsourced testing

The rapid growth of mobile devices has ushered in an era of different device platforms. Different devices require a consistent user experience, especially with similar graphical user interfaces (GUIs). However, the different code bases of the various operating systems as well as the different GUI layouts and resolutions of the various devices pose a challenge for automated software testing. Crowdsourced software testing (CST) has emerged as a viable solution where crowdsourced workers perform tests on their own devices and provide detailed bug reports. Although CST is cost-effective, it is not very efficient and requires a large number of workers for manual testing. The potential of optimizing CST reproduction testing through computer vision remains largely untapped, especially when considering the uniformity of GUI elements on different devices. In this study, we present a novel deep learning model specifically designed to re-identify GUI elements in CST reproduction test scenarios, regardless of the underlying code changes on different devices. The model features a robust backbone network for feature extraction, an innovative attention mechanism with learnable factors to enhance the features of GUI elements and minimize interference from their backgrounds, and a classifier to determine matching labels for these elements. Our approach was validated on a large GUI element dataset containing 31,098 element images for training, 115,704 element images from real apps for testing, and 67 different background images. The results of our experiments underline the excellent accuracy of the model and the importance of each component. This work is a major step forward in improving the efficiency of reproduction testing in CST. The innovative solutions we propose could further reduce labor costs for CST platforms.

Uncovering Hidden Dependencies: Constructing Intelligible Path Witnesses using Dataflow Analyses

The lack of sound, concise and comprehensive error reports emitted by a static analysis tool can cause increased fixing cost, bottleneck at the availability of experts and even may undermine the trust in static analysis as a method. This paper presents novel techniques to improve the quality of bug reports for static analysis tools that employ symbolic execution. With the combination of data and control dependency analysis, we can identify the relevance of particular code snippets that were previously missing from the report. We demonstrated the benefits of our approach by implementing an improved bug report generator algorithm for the Clang Static Analyzer. After being tested by the open source community our solution became enabled by default in the tool.

A New Method of Security Bug Reports Analysis

A New Method of Security Bug Reports Analysis

Software bug localization based on optimized and ensembled deep learning models

AbstractAn automated task for finding the essential buggy files among software projects with the help of a given bug report is termed bug localization. The conventional approaches suffer from the challenges of performing lexical matching. Particularly, the terms utilized for describing the bugs in the bug reports are observed to be irrelevant to the terms used in the source code files. To resolve these problems, we propose an optimized and ensemble deep learning model for software bug localization. These features are reduced by the principle component analysis (PCA). Then, they are selected by the weighted convolutional neural network (CNN) model with the support of the Modified Scatter Probability‐based Coyote Optimization Algorithm (MSP‐COA). Finally, the optimal features are subjected to the ensemble deep neural network and long short‐term memory (DNN‐LSTM), with parameter tuning by the MSP‐COA. Experimental results show that the proposed approach can achieve higher bug localization accuracy than individual models.

BushraDBR: An Automatic Approach to Retrieving Duplicate Bug Reports

BushraDBR: An Automatic Approach to Retrieving Duplicate Bug Reports

Two-Level Information-Retrieval-Based Model for Bug Localization Based on Bug Reports

Software bugs are a noteworthy concern for developers and maintainers. When a failure is detected late, it costs more to be fixed. To repair the bug that caused the software failure, the location of the bug must first be known. The process of finding the defective source code elements that led to the failure of the software is called bug localization. Effective approaches for automatically locating bugs using bug reports are highly desirable, as they would reduce bug-fixing time, consequently lowering software maintenance costs. With the increasing size and complexity of software projects, manual bug localization methods have become complex, challenging, and time-consuming tasks, which motivates research on automated bug localization techniques. This paper introduces a novel bug localization model, which works on two levels. The first level localizes the buggy classes using an information retrieval approach and it has two additional sub-phases, namely the class-level feature scoring phase and the class-level final score and ranking phase, which ranks the top buggy classes. The second level localizes the buggy methods inside these classes using an information retrieval approach and it has two sub-phases, which are the method-level feature scoring phase and the method-level final score and ranking phase, which ranks the top buggy methods inside the localized classes. A model is evaluated using an AspectJ dataset, and it can correctly localize and rank more than 350 classes and more than 136 methods. The evaluation results show that the proposed model outperforms several state-of-the-art approaches in terms of the mean reciprocal rank (MRR) metrics and the mean average precision (MAP) in class-level bug localization.

An empirical assessment of different word embedding and deep learning models for bug assignment

Bug assignment, or bug triage, focuses on identifying the appropriate developers to repair newly discovered bugs, thereby managing them more effectively. Several deep learning-based approaches have been proposed for automated bug assignment. These approaches view automated bug assignment as a text classification task — the textual description of a bug report is utilized as the input and the potential fixers are regarded as the output labels. Such approaches typically depend on the classification performance of natural language processing and machine learning techniques. Various word embedding and deep learning models have emerged continuously. The effectiveness of those approaches depends on the chosen deep learning model, used for classification, and the word embedding model, used for representing bug reports. However, prior research does not empirically evaluate the impacts of various word embedding and deep learning models for automated bug assignment. In this paper, we conduct an empirical study to analyze the performance variations among 35 deep learning-based automated bug assignment approaches. These approaches are based on five word embedding techniques, i.e., Word2Vec, GloVe, NextBug, ELMo, and BERT, and seven text classification models, i.e., TextCNN, LSTM, Bi-LSTM, LSTM with attention, Bi-LSTM with attention, MLP, and Naive Bayes. We evaluated these combinations across three benchmark datasets, namely Eclipse JDT, GCC, and Firefox, and their mergence i.e., a cross-project dataset. Our main observations are: (1) Bi-LSTM with attention and Bi-LSTM using ELMo are significantly superior to other deep learning models on bug assignment tasks in terms of top-k (k = 1, 5, 10) accuracy and MRR; (2) Both the summary and description of bug reports are useful for bug assignment, but the description is more useful than the summary; (3) The training corpus for word embedding models has a significant impact on the performance of deep learning-based bug assignment methods. Our results show the importance of tuning different components (e.g. word embedding model, classification model, and textual input) in deep learning-based automated bug assignment methods and provide important insights for practitioners and researchers.

Summarization of Software Bug Report based on Sentence Semantic Similarity (SSBRSSS) Technique

In this work, sentence similarity between sentences of software bug report is computed. For this, two methods are utilized, Latent Semantic Analysis and Text Rank. Latent Semantic Analysis is used to compute semantic similarity between sentences of bug reports which infers deeper and hidden relation between words. From this, a pair of sentences with semantic similarity above a set threshold is selected and from one pair of sentences, only one sentence is selected. The remaining sentences are passed into TextRank algorithm and sentences with high similarity are further selected to generate a coherent summary. The proposed approach is evaluated on a newly constructed Apache Project Bug Report Corpus and existing Bug Report Corpus. The proposed approach is also compared with baseline approaches that mainly focus on only lexical similarity. The results when evaluated on Apache project Bug Report Corpus attains an average value of 80%, 72.57%, 76.05% and 76.57% in terms of precision, recall, F-score and pyramid precision respectively.

NATURE-BASED PREDICTION MODEL OF BUG REPORTS BASED ON ENSEMBLE MACHINE LEARNING MODEL

The rapid growth of software systems has led to an increase in the volume and complexity of bug reports, necessitating efficient and accurate prediction models to manage and address these reports effectively. This paper presents a Nature-Based Prediction Model of Bug Reports (NBPMBR) leveraging ensemble machine learning techniques to enhance the prediction accuracy and reliability of bug report classifications. By integrating various nature-inspired algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) within an ensemble framework, NBPMBR combines the strengths of these algorithms to improve performance.The model undergoes rigorous training on historical bug report data, using feature extraction methods to capture relevant information such as bug severity, priority, and textual descriptions. The ensemble approach ensures robustness by mitigating the weaknesses of individual algorithms, leading to a more balanced and accurate prediction outcome. Experimental results on benchmark datasets demonstrate that NBPMBR outperforms traditional machine learning models in terms of precision, recall, and overall prediction accuracyThis nature-based ensemble model not only advances the state-of-the-art in bug report prediction but also provides a scalable and adaptable solution for real-world software maintenance and quality assurance processes. By automating the classification and prioritization of bug reports, NBPMBR aids in the efficient allocation of resources, thereby improving the software development lifecycle and enhancing the overall quality of software products.

An Integrated Approach using Developer Profiles with Temporal Dynamics for Assignee Recommendation in Non-Reproducible Bugs

A vital component of the software framework is bug reports. Bug tracking systems are employed for handling and closing the reported bugs. Unfxed bugs hinder software reliability and also camoufage other bugs. Non-Reproducible bugs deteriorate the quality of bug fxation significantly. Many times non-reproducible bugs are reopened and they lead to fxation as well. This improves the overall bug handling process. The fxation of re-opened non-reproducible bugs requires selection of a prominent developer having enough expertise for resolving the issue. This paper presents a novel framework for triaging of non-reproducible bugs called TriageExpert-Recency. The proposed framework, TriageExpert-Recency takes developer’s expertise and time recency of usage of topic by developer into consideration while selecting an appropriate developer. The developer’s expertise corresponding to a topic is computed on the basis of number of times developer worked on a similar topic. The time recency of usage of topic by developer is computed using the dates of historical bugs handled by the developer. Bug reports are gathered from the Mozilla Firefox and SeaMonkey projects’ Bugzilla repositories for experimental review. Experimental results showed a hit ratio up to 68% for Top-5 developer list using TriageExpert-Recency framework for developer selection in Non-Reproducible bugs. he proposed approach achieved performance up to 70.18% for Mozilla Firefox and 74.8% for SeaMonkey projects respectively. Overall, it has been found that the proposed approach outperforms the existing techniques.

NATURE-BASED PREDICTION MODEL OF BUG REPORTS BASED ON ENSEMBLE MACHINE LEARNING MODEL

The rapid growth of software systems has led to an increase in the volume and complexity of bug reports, necessitating efficient and accurate prediction models to manage and address these reports effectively. This paper presents a Nature-Based Prediction Model of Bug Reports (NBPMBR) leveraging ensemble machine learning techniques to enhance the prediction accuracy and reliability of bug report classifications. By integrating various nature-inspired algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) within an ensemble framework, NBPMBR combines the strengths of these algorithms to improve performance.The model undergoes rigorous training on historical bug report data, using feature extraction methods to capture relevant information such as bug severity, priority, and textual descriptions. The ensemble approach ensures robustness by mitigating the weaknesses of individual algorithms, leading to a more balanced and accurate prediction outcome. Experimental results on benchmark datasets demonstrate that NBPMBR outperforms traditional machine learning models in terms of precision, recall, and overall prediction accuracyThis nature-based ensemble model not only advances the state-of-the-art in bug report prediction but also provides a scalable and adaptable solution for real-world software maintenance and quality assurance processes. By automating the classification and prioritization of bug reports, NBPMBR aids in the efficient allocation of resources, thereby improving the software development lifecycle and enhancing the overall quality of software products.

Towards Unveiling Exploitation Potential With Multiple Error Behaviors for Kernel Bugs

Nowadays, fuzz testing has significantly expedited the vulnerability discovery of Linux kernel. Security analysts use the manifested error behaviors to infer the exploitability of one bug and thus prioritize the patch development. However, only using an error behavior in the report, security analysts might underestimate the exploitability of the kernel bug because it could manifest various error behaviors indicating different exploitation potentials. In this work, we conduct an empirical study on multiple error behaviors of kernel bugs to understand 1) the prevalence of multiple error behaviors and the possible impact of multiple error behaviors towards the exploitation potential; 2) the factors that manifest multiple error behaviors with different exploitation potential. We collected <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all the fixed kernel bugs</i> reported on Syzbot from September 2017 to January 2022, including 3,352 bug reports. We observed that multiple error behaviors manifested by kernel bugs are prevalent in the real world, and more error behaviors help unveil the exploitability of kernel bugs. Then we organized Linux kernel experts to analyze a sample of kernel bug dataset (484 bug reports, unique 162 bugs) and identified 6 key contributing factors to the mutiple error behaviors. Finally, based on the empirical findings, we propose an object-driven fuzzing technique to explore all possible error behaviors that a kernel bug might bring about. To evaluate the utility of our proposed technique, we implement our fuzzing tool <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GREBE</monospace> and apply it to 60 real-world Linux kernel bugs. On average, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GREBE</monospace> could manifest 2+ additional error behaviors for each of the kernel bugs. For 26 kernel bugs, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GREBE</monospace> discovers higher exploitation potential. We report to kernel vendors some of the bugs – the exploitability of which was wrongly assessed and the corresponding patch has not yet been carefully applied – resulting in their rapid patch adoption.

A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection

A Comparative Study of Contemporary Learning Paradigms in Bug Report Priority Detection

Enhancing Bug Report Summaries Through Knowledge-Specific and Contrastive Learning Pre-Training

Enhancing Bug Report Summaries Through Knowledge-Specific and Contrastive Learning Pre-Training

Optimal Launch Timing of Bug Bounty Programs for Software Products under Different Licensing Models

An increasing number of software firms are utilizing bug bounty programs (BBPs) to detect bugs and enhance their product quality by leveraging the contributions of external ethical hackers. Although launching a BBP involves bounties as well as the costs of processing bug reports and fixing bugs, software firms can save failure costs and enjoy the benefits of greater user trust. The costs and benefits resulting from launching a BBP vary with launch timings and software licensing models. Hence, we investigate the optimal BBP launch strategies for software firms, using perpetual or subscription licensing models. Our findings reveal that under perpetual licensing, the firm has only two viable launch strategies: simultaneous launch, i.e., launching the software and the BBP simultaneously, and no launch. Under subscription licensing, however, delayed launch, i.e., launching the BBP later than the software release time, occurs as the optimal strategy when the failure cost is not high and the benefit of user trust is significant. Two distinct patterns in the relationship between the firm’s bug-fixing capability and its payoff are identified: a U-shaped pattern and an inverted U-shaped pattern. We uncover the conditions under which a firm should opt not to launch a BBP as its bug-fixing capability improves. This study offers insights into how firms can be motivated to launch BBPs to improve the overall reliability of their software.

SumLLaMA: Efficient Contrastive Representations and Fine-Tuned Adapters for Bug Report Summarization

SumLLaMA: Efficient Contrastive Representations and Fine-Tuned Adapters for Bug Report Summarization