Bug Report Data 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.

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.

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.

Enhancing Bug Localization Using Phase-Based Approach

Software bug localization is an important step in the software maintenance process. Automatic bug localization can reduce the time consumed in the process of localization. Some techniques are applied in the bug localization process, but those techniques suffer from limitations in time and accuracy. This paper proposes a phase-based bug localization approach to overcome these limitations. The approach is composed of three main phases which are raw data preparation, package classification, and source code recommendation. The main input to our approach is a bug report and the source code of the past versions for the target system of interest. From the bug report, various information is utilized: the summary, the description, the stack traces, and the fixed source code files. The raw data preparation phase is used to restructure those inputs. The package classification phase aims to locate the package that would include the source code to be modified as a first step, hence reducing the time needed to locate the source code file due to the lexical mismatch between those files and the bug report data. Bidirectional Encoder Representations from Transformers (BERT), which is a sentence embedding technique, is utilized in the package classification and source code recommendation phases. The experimental results show that our approach outperforms existing approaches according to TOP-N rank and Mean Reciprocal Rank (MRR) evaluation metrics.

Evaluating the Veracity of Software Bug Reports using Entropy-based Measures

The wide usage of open source software (OSS) results in an increase of bug data forming an integral part of the extensive data ecosystem. This bug report data needs to be analyzed for bug fixing and prediction of various important attributes like bug severity, priority, fix time, assignees, etc. The increased volume of bug data and different bug reporters from different geographical locations make veracity an important concern. We assume that the bug reports (i.e., different bug attributes) reported in software bug repositories are trustworthy during the bug triaging process. In reality, the bug report data are not trustworthy regarding various aspects like integrity, authenticity, and trusted origin as the bugs are reported by users who may or may not have proper knowledge of the software. In this paper, we proposed entropy-based models for veracity estimation of different bug attributes.

Two-Phase Defect Detection Using Clustering and Classification Methods

Autonomous fault management of network and distributed systems is a challenging research problem and attracts many research activities. Solving this problem heavily depends on expertise knowledge and supporting tools for monitoring and detecting defects automatically. Recent research activities have focused on machine learning techniques that scrutinize system output data for mining abnormal events and detecting defects. This paper proposes a two-phase defect detection for network and distributed systems using log messages clustering and classification. The approach takes advantage of K-means clustering method to obtain abnormal messages and random forest method to detect the relationship of the abnormal messages and the existing defects. Several experiments have evaluated the performance of this approach using the log message data of Hadoop Distributed File System (HDFS) and the bug report data of Bug Tracking System (BTS). Evaluation results have disclosed some remarks with lessons learned.

Placing volunteered geographic health information: Socio-spatial bias in 311 bed bug report data for New York City.

Health researchers and policy-makers increasingly use volunteered geographic information (VGI) to analyze spatial variation in health and wellbeing and to develop interventions. As socially constructed data, health VGI reflect the people who perceive issues and choose to report them, and the digital systems that structure the reporting process. We propose a conceptual framework that describes the interlocking effects of socioeconomic, behavioral, geographic, and technological processes on VGI accuracy and credibility. GIS and statistical methods are used to analyze social and geographical biases in health-related VGI through a case study of bed bug complaint data from New York City's 311 system. Reports of bed bug infestation from 311 are mapped and modeled to uncover associations with socioeconomic and built environment characteristics. Factors associated with bed bug report credibility are examined by comparing characteristics of confirmed reports with those for reports in which inspectors found no evidence of infestation (negative reports). A multilevel model of credibility incorporating report-, building-, and tract-level variables reveals strong geographical and socioeconomic biases, with negative reports generated more frequently from high-value residential buildings located in high-income neighborhoods with predominately white, non-Hispanic populations. Using 311 data for all bed bug reports, rather than confirmed reports, obscures the burden of these pests in high poverty neighborhoods and diminishes socioeconomic disparities. Mistaken reporting also has economic costs, as each report triggers an inspection by city inspectors that entails time, monetary, and opportunity costs.

Analysis of Various Types of Bugs in the Object Oriented Java Script Language Coding

Objective: The main goal of this work is to categorize and understand the various types of bugs present in the Object oriented java scripting language. By finding these faults one can find out the nature of faults which causes the run time failure of programs. Methods: There are various types of bugs present in the OOJS environment which needs to be categorized well to understand the nature of fault which has occurred in the OOJS language. By doing so, web pages can be prevented from the functioning failure and can cause the generation of more user flexible environment. Fault Localization is the approach to analyse and detect the place of faults present in the java scripting language. Transductive Support Vector Machine (TSVM) classification algorithm is introduced to categorize the faults into various sub types by classifying them based on types of bugs. This classification is done on the bug report data set which was created by using the fault localization approach. Findings: The experimental test conducted proves that the proposed approach named TSVM based categorization of faults can detect the faults efficiently which can be used further for error detection. The proposed approach improves in its performance in terms of improved accuracy detection by categorizing the faults correctly through which faults present in the programming language can be done efficiently. Conclusion: From the findings it can be concluded that the proposed approach improved in its performance in terms of improved accuracy.

An Empirical Comparison of Machine Learning Techniques in Predicting the Bug Severity of Open and Closed Source Projects

Bug severity is the degree of impact that a defect has on the development or operation of a component or system, and can be classified into different levels based on their impact on the system. Identification of severity level can be useful for bug triager in allocating the bug to the concerned bug fixer. Various researchers have attempted text mining techniques in predicting the severity of bugs, detection of duplicate bug reports and assignment of bugs to suitable fixer for its fix. In this paper, an attempt has been made to compare the performance of different machine learning techniques namely Support vector machine (SVM), probability based Naïve Bayes (NB), Decision Tree based J48 (A Java implementation of C4.5), rule based Repeated Incremental Pruning to Produce Error Reduction (RIPPER) and Random Forests (RF) learners in predicting the severity level (1 to 5) of a reported bug by analyzing the summary or short description of the bug reports. The bug report data has been taken from NASA’s PITS (Projects and Issue Tracking System) datasets as closed source and components of Eclipse, Mozilla & GNOME datasets as open source projects. The analysis has been carried out in RapidMiner and STATISTICA data mining tools. The authors measured the performance of different machine learning techniques by considering (i) the value of accuracy and F-Measure for all severity level and (ii) number of best cases at different threshold level of accuracy and F-Measure.