Knowledge Of Bugs Research Articles

An automated fault detection system for communication networks and distributed systems

Automating fault detection in communication networks and distributed systems is a challenging process that usually requires the involvement of supporting tools and the expertise of system operators. Automated event monitoring and correlating systems produce event data that is forwarded to system operators for analyzing error events and creating fault reports. Machine learning methods help not only analyzing event data more precisely but also forecasting possible error events by learning from existing faults. This study introduces an automated fault detection system that assists system operators in detecting and forecasting faults. This system is characterized by the capability of exploiting bug knowledge resources at various online repositories, log events and status parameters from the monitored system; and applying bug analysis and event filtering methods for evaluating events and forecasting faults. The system contains a fault data model to collect bug reports, a feature and semantic filtering method to correlate log events, and machine learning methods to evaluate the severity, priority and relation of log events and forecast the forthcoming critical faults of the monitored system. We have evaluated the prototyping implementation of the proposed system on a high performance computing cluster system and provided analysis with lessons learned.

Constructing Bug Knowledge Graph as a Service for Bug Search

When encountering bug issues, developers tend to search the bug repository and commit repository for references. However, the links between bug reports and commits in version control systems are often missed, and the information in bug repository and commit repository can provide is simple. When developers search a bug issue, they can only get the information of bug reports or commits, which are loose and difficult for developers to refer. What’s more, many searching results are not accurate. To deal with these problems, this paper proposes an approach to deal with the bug and commit information with the topic model, and construct bug knowledge graph as a service to assist in bug search. In addition, as the amount of bug related information continuously increase, it is time-consuming to update the data. We can automatically update the bug knowledge graph with the LTM topic model (a lifelong topic model). Finally, the experiment with the bug reports from Bugzilla@Mozilla and the corresponding commits from Github was conducted. The experiment results show that our approach is effective and efficient to help developers search relevant bugs for reference by constructing the bug knowledge as a service.

Towards an understanding of change types in bug fixing code

ContextAs developing high quality software becomes increasingly challenging because of the explosive growth of scale and complexity, bugs become inevitable in software systems. The knowledge of bugs will naturally guide software development and hence improve software quality. As changes in bug fixing code provide essential insights into the original bugs, analyzing change types is an intuitive and effective way to understand the characteristics of bugs. ObjectiveIn this work, we conduct a thorough empirical study to investigate the characteristics of change types in bug fixing code. MethodWe first propose a new change classification scheme with 5 change types and 9 change subtypes. We then develop an automatic classification tool CTforC to categorize changes. To gain deeper insights into change types, we perform our empirical study based on three questions from three perspectives, i.e. across project, across domain and across version. ResultsBased on 17 versions of 11 systems with thousands of faulty functions, we find that: (1) across project: the frequencies of change subtypes are significantly similar across most studied projects; interface related code changes are the most frequent bug-fixing changes (74.6% on average); most of faulty functions (65.2% on average) in studied projects are finally fixed by only one or two change subtypes; function call statements are likely to be changed together with assignment statements or branch statements; (2) across domain: the frequencies of change subtypes share similar trends across studied domains; changes on function call, assignment, and branch statements are often the three most frequent changes in studied domains; and (3) across version: change subtypes occur with similar frequencies across studied versions, and the most common subtype pairs tend to be same. ConclusionOur experimental results improve the understanding of changes in bug fixing code and hence the understanding of the characteristics of bugs.

A Survey on Effective Bug Prioritizing using Instance Selection and Feature Selection Techniques

Background/Objectives: This paper represents a review of how to fix bugs and how to accurately assign a brand new bug to a particular developer. Software firms pay over 45 % of value in coping with computer code bugs. A complicated process of fixing bugs is bug triage that aims to accurately assign a developer to a brand new bug. To diminish the time value in human work, text classification techniques are used to conduct automatic bug sorting. Methods: Findings: In this paper, we possess a mind set to address the problem of knowledge reduction for bug sorting, i.e., the means to prune back the ratio and improve the standard of bug knowledge. We associate instance selection with feature selection to at the same time cut back knowledge ratio on the bug dimension and also the word dimension. To make away the parliamentary procedure of applying instance selection and feature election, we extract attributes from historical bug knowledge sets and build a prophetic model for a brand new bug knowledge set. We induce a tendency to thorough empirical observation and to investigate the execution of data reduction of the whole 600,000 bug reports of 2 giant open supply comes, namely Eclipse and Mozilla. Data reduction techniques such as classification and prediction method can be used to cut down the large bug dataset (by removing noisy and irrelevant data), and predict the correct answer for the bugs by applying keywords and properties. A classification method such as Bayesian Classification is used to classify the incoming bug dataset. Properties of Instance Selection and Feature Selection techniques are merged along with the attributes and keywords to solve the problem of bug prioritizing. Applications/Improvements: These outcomes demonstrate that the data reduction will effectively cut back the knowledge ratio and improve the accuracy of bug sorting. Our work provides associate approach to investment techniques on processing to create decreased and high-quality bug expertise in computer code development and sustenance. Regression analysis techniques can also be applied to a decision table to calculate the effort that is needed to resolve an incoming bug.

Software Bug Ontology Supporting Semantic Bug Search on Peer-to-Peer Networks

This article presents a semantic bug search system that assists users in sharing and searching solutions for similar bug reports on peer-to-peer networks. This system features the capability of exploring bug knowledge resources at various sites in distributed environment and exploiting the classification and relationship information of bug reports. The system uses a unified bug schema that not only integrates bug reports from various bug knowledge resources into a single database but also contains several selective properties including package dependencies, bug relationships, bug symptoms and categories to foster a semantic search mechanism. We have implemented several components of the system including bug updater to maintain bug database, query handler to share and search bug reports, and peer controller to manage communication on an appropriate peer-to-peer network. We have experimented the system on a distributed computing testbed and measured its feasibility, scalability and efficiency.

Communicating bugs: global bug knowledge distribution

Unfortunately, software-component libraries shared on a global scale contain bugs. Members of the library user community often report bugs, workarounds, and fixes. This bug knowledge, however, generally remain undiscovered on library web site or in open bug databases.In this article I describe design criteria for bug handing from a global user community perspective. I also describe a distribution architecture for bug knowledge. The architecture focuses on bug awareness and bug visibility in the standard work environment.

Representation of teaching‐world knowledge and the method of the inference for intelligent cai

AbstractThis paper considers the learning world of differential calculus for composite functions, including trigonometric functions, and describes the mechanism of an intelligent CAI system NEUTON. In the design of the system, the teaching world is divided into the meta‐knowledge‐base for dialog management, the text knowledge‐base, the problem‐solving expert knowledge, the rule‐base for tutoring strategy, and the bug knowledge. The modular structure is thus considered for the knowledge‐base. The representation of knowledge and the method of inference are considered, utilizing the characteristics of such knowledge as follows.The text knowledge is based on the semantic network representation, where the procedural knowledge is supplemented. Other knowledge bases are described by the production rules. The knowledge structure is simplified so that the interpretation and the execution are made by a single meta‐inference engine. The response of the student as well as his level of understanding are recognized and diagnosed utilizing those knowledges, and the individual guidance process is developed. In the intelligent CAI, important mechanisms are those for identifying the cause of the error, and the determination of the micro‐ and macroscopic means of remedy from the knowledge‐base. In this paper, a schema is described where the bug generation, diagnosis mechanism, problem‐solving expert mechanism and the student model called MOD are closely interrelated.