SFSCon19 - Kristian Schwienbacher - Custom error and event tracking for Ember js apps
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This bachelor's thesis discusses a system for identifying bug-inducing commits based on user activity logs from an Ember.js application. The system includes an error tracking addon to log errors and user events, which are sent to a measurement server. The server analyzes the stack traces to determine the bug-inducing commit and compares this approach to the traditional SZZ algorithm. The thesis evaluates the system through data exploration and case studies, finding it was able to more accurately identify the bug-inducing commit than the SZZ algorithm when stack traces were available.
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Assignment 1 Week 2.docx1Assignment 1 Topic Selection.docx
Assignment 1 Week 2.docx
1
Assignment 1: Topic Selection
Assignment 1: Topic Selection
Software Engineering: The Autotest Framework
Jessica Hill Scott
Dr. Teresa Wilburn
RES 531: Research Methods
January 12th 2014
Topic Selection: Software Engineering: Automated Testing/Programs That Test Themselves
Topic Description:
Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Although crucial to software quality and widely deployed by programmers and testers, software testing still remains an art, due to limited understanding of the principles of software. The difficulty in software testing stems from the complexity of software: we cannot completely test a program with moderate complexity. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a trade-off between budget, time and quality (Hetzel). Automated testing is a widely used phrase. To understand what it entails, it is necessary to distinguish several increasingly ambitious levels of automation. What is best automated today is test execution. In a project that has generated thousands of test cases, running them manually would be tedious, especially as testing campaigns occur repeatedly. For example, it is customary to run extensive tests before every release. Traditionally, testers wrote scripts to run the tests. A related goal, also addressed by some of today’s tools, is regression testing. It is a common phenomenon of software development that some corrected faults reappear in later versions, indicating that the software has partly “regressed.” A project should retain any test that failed at any stage of its history, then passed after the fault was corrected; test campaigns should run all such tests to spot cases of regression. Automated tools should provide resilience. A large test suite is likely to contain some test cases that, in a particular execution, crash the program. Resilience means that the process may continue anyway with the remaining cases. One of the most tedious aspects of testing is test case generation. With modern computers we can run very large numbers of test cases. Usually, developers or testers have to devise them; this approach, limited by people’s time, does not scale up. Commonly used frameworks mostly address the first three goals: test execution, regression testing, and resilience. They do not address the most labor-intensive tasks: preparing test cases, possibly in a minimized form, and interpreting test results. Without progress on these issues, testing confronts a paradox: While the growth of computing power should enable us to perform ever more exhaustive tes ...
Software engineering practices and software quality empirical research results
This presentation summarizes empirical research findings in software engineering practices including test driven development, peer code reviews, and defect prediction.
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We propose a methodology to build a "developer-informed" oracle for the evaluation of SZZ variants. We use Natural Language Processing (NLP) to identify bug-fixing commits in which developers explicitly reference the commit(s) that introduced a fixed bug. This was followed by a manual filtering step aimed at ensuring the quality and accuracy of the oracle. Once built, we used the oracle to evaluate several variants of the SZZ algorithm in terms of their accuracy. Our evaluation helped us to distill a set of lessons learned to further improve the SZZ algorithm.
Giovanni Rosa (University of Molise), Luca Pascarella (Università della Svizzera italiana), Simone Scalabrino (University of Molise), Rosalia Tufano (Università della Svizzera Italiana), Gabriele Bavota (Software Institute, USI Università della Svizzera italiana), Michele Lanza (Software Institute, USI Università della Svizzera italiana), Rocco Oliveto (University of Molise),
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* IEEE Digital Library: https://www.computer.org/csdl/proceedings-article/icse/2021/029600a436/1sEXo2zV4OI
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He has been paid to write software for over 30 years, and has spent most of his time recently in the platform engineering space. He’s a keen advocate of XP values and practices, and sees himself more as an engineer than craftsman.
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Similar to SFSCon19 - Kristian Schwienbacher - Custom error and event tracking for Ember js apps
Assignment 1 Week 2.docx1Assignment 1 Topic Selection.docx
Assignment 1 Week 2.docx
1
Assignment 1: Topic Selection
Assignment 1: Topic Selection
Software Engineering: The Autotest Framework
Jessica Hill Scott
Dr. Teresa Wilburn
RES 531: Research Methods
January 12th 2014
Topic Selection: Software Engineering: Automated Testing/Programs That Test Themselves
Topic Description:
Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Although crucial to software quality and widely deployed by programmers and testers, software testing still remains an art, due to limited understanding of the principles of software. The difficulty in software testing stems from the complexity of software: we cannot completely test a program with moderate complexity. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a trade-off between budget, time and quality (Hetzel). Automated testing is a widely used phrase. To understand what it entails, it is necessary to distinguish several increasingly ambitious levels of automation. What is best automated today is test execution. In a project that has generated thousands of test cases, running them manually would be tedious, especially as testing campaigns occur repeatedly. For example, it is customary to run extensive tests before every release. Traditionally, testers wrote scripts to run the tests. A related goal, also addressed by some of today’s tools, is regression testing. It is a common phenomenon of software development that some corrected faults reappear in later versions, indicating that the software has partly “regressed.” A project should retain any test that failed at any stage of its history, then passed after the fault was corrected; test campaigns should run all such tests to spot cases of regression. Automated tools should provide resilience. A large test suite is likely to contain some test cases that, in a particular execution, crash the program. Resilience means that the process may continue anyway with the remaining cases. One of the most tedious aspects of testing is test case generation. With modern computers we can run very large numbers of test cases. Usually, developers or testers have to devise them; this approach, limited by people’s time, does not scale up. Commonly used frameworks mostly address the first three goals: test execution, regression testing, and resilience. They do not address the most labor-intensive tasks: preparing test cases, possibly in a minimized form, and interpreting test results. Without progress on these issues, testing confronts a paradox: While the growth of computing power should enable us to perform ever more exhaustive tes ...
Software engineering practices and software quality empirical research results
This presentation summarizes empirical research findings in software engineering practices including test driven development, peer code reviews, and defect prediction.
IRJET- A Detailed Analysis on Windows Event Log Viewer for Faster Root Ca...
This document summarizes research on analyzing Windows event logs to identify the root causes of defects in software. It discusses using machine learning algorithms and pattern recognition techniques on event log data to detect defect root causes. Specifically, it proposes developing an efficient algorithm based on pattern recognition to accurately detect defect root causes. The algorithm would analyze past event logs and defect resolution methods to improve prediction capability and accuracy over traditional approaches. It also reviews literature on using clustering, classification, and other machine learning methods on event logs to identify patterns and anomalies.
Using Fuzzy Clustering and Software Metrics to Predict Faults in large Indust...
This document describes a study that uses fuzzy clustering and software metrics to predict faults in large industrial software systems. The study uses fuzzy c-means clustering to group software components into faulty and fault-free clusters based on various software metrics. The study applies this method to the open-source JEdit software project, calculating metrics for 274 classes and identifying faults using repository data. The results show 88.49% accuracy in predicting faulty classes, demonstrating that fuzzy clustering can be an effective technique for fault prediction in large software systems.
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
F017652530
This document provides an overview of software fault detection and prevention mechanisms. It discusses several fault detection mechanisms used in the software development lifecycle, including automated static analysis, graph mining, and classifiers. Automated static analysis tools can find standard problems but miss many faults that could lead to failures. Graph mining uses call graph analysis to identify issues in function calling frequencies or structures. Classifiers like NaiveBayes can be trained on normal code behavior to identify abnormal events. The document also discusses fault prevention benefits, related work, and concludes with the importance of fault detection and prevention for developing high quality, reliable software.
Adaptation of the technology of the static code analyzer for developing paral...
This document discusses the adaptation of static code analysis tools for developing parallel programs. Static code analysis was originally introduced in the 1970s-1980s as a complement to compilers but declined in popularity in the 1990s as compiler diagnostics improved. However, interest has increased again as modern static analyzers can detect more complex errors, such as unsafe data access from multiple threads in parallel programs. The document examines how static analysis tools can help simplify the process of creating parallel program solutions by detecting errors even in rarely executed code sections.
IRJET- Data Reduction in Bug Triage using Supervised Machine Learning
This document discusses using machine learning techniques for automatic bug triage to reduce the time and costs associated with manually assigning software bugs to developers. It proposes using data reduction techniques like feature selection and instance selection to create a smaller, higher quality bug repository by removing redundant bug reports and words. This reduced dataset would then be used to train a classifier to automatically suggest the most suitable developer for a given new bug, aiming to improve prediction accuracy while reducing training and prediction time compared to using the full dataset.
Software reliability
The document discusses software reliability and reliability growth models. It defines software reliability and differentiates it from hardware reliability. It also describes some commonly used software reliability growth models like Musa's basic and logarithmic models. These models make assumptions about fault removal over time to predict how failure rates will change as testing progresses. The key challenges with models are uncertainty and accurately estimating their parameters.
Finding Bugs, Fixing Bugs, Preventing Bugs — Exploiting Automated Tests to In...
Finding Bugs, Fixing Bugs, Preventing Bugs — Exploiting Automated Tests to In...University of Antwerp
With the rise of agile development, software teams all over the world embrace faster release cycles as *the* way to incorporate customer feedback into product development processes. Yet, faster release cycles imply rethinking the traditional notion of software quality: agile teams must balance reliability (minimize known defects) against agility (maximize ease of change). This talk will explore the state-of-the-art in software test automation and the opportunities this may present for maintaining this balance. We will address questions like: Will our test suite detect critical defects early? If not, how can we improve our test suite? Where should we fix a defect?
(Keynote for the SHIFT 2020 and IWSF 2020 Workshops, October 2020)An Empirical Comparison of the Development History of CloudStack and Eucalyptus
This document analyzes and compares the development histories of the open source cloud platforms Eucalyptus and CloudStack. It seeks to understand how these systems have grown and evolved over time, how testing has changed, and how issue and bug reports are managed. The methodology involves analyzing commits, source code, and issue/bug reports for each project. Key findings include that both systems grew at similar rates, CloudStack has better test coverage, and the projects have different processes for resolving bugs.
Christopher N. Bull History-Sensitive Detection of Design Flaws B ...
This document is a dissertation submitted by Christopher N. Bull for the degree of Bachelor of Computer Science with Software Engineering. The dissertation presents Jistory, a tool that implements a history-sensitive strategy for automatically detecting the design flaw of god classes in software systems. The document includes chapters on background research, a feasibility study of detection strategies, the design and implementation of Jistory, testing and evaluation of Jistory, and conclusions. The aim of the project is to evaluate whether history-sensitive detection strategies can improve the detection of design flaws compared to conventional strategies and whether such strategies can be automated to provide accurate results.
Week1.pptx
This document provides an overview of advance software engineering concepts. It discusses recommended books on software engineering and common software engineering activities like systems analysis and design. It also discusses key software engineering challenges like increasing diversity and demands for reduced delivery times. Different software development lifecycles are covered, including the waterfall model. Frequently asked questions about software engineering concepts are also answered. Agile software development practices like daily stand-ups, iteration planning, and test-driven development are explained.
Automation in the Bug Flow - Machine Learning for Triaging and Tracing
Issue management is a costly part of software development. In large projects, the continuous inflow of issue reports contributes to the information overload in a project, i.e., "a state where individuals do not have time or capacity to process all available information". In issue triaging, an initial step in issue management, a developer must be able to overview existing issue reports and easily navigate the software engineering project landscape. In this presentation, we present support for two work tasks involved in issue management: 1) issue assignment and 2) change impact analysis. We use machine learning to harness the ever-growing number of issue reports, by training recommendation systems on previous issues. Our industrial evaluations on 50,000+ issue reports in two large software development organizations indicate that automated issue assignment performs in line with current manual work. Moreover, we present how traceability from already resolved issue reports to various artifacts can be reused to jump start change impact analyses for newly submitted issues. Finally, we speculate on future ways to tame information overload into helpful software engineering recommendations.
Embedded software static analysis_Polyspace-WhitePaper_final
This document discusses the challenges of testing embedded software and the limitations of traditional techniques like manual code reviews and dynamic testing. It introduces Polyspace Bug Finder and Polyspace Code Prover as static analysis tools that can overcome these limitations by automatically finding bugs, proving the absence of runtime errors, and providing stronger assurance of code reliability compared to non-exhaustive testing methods. The document argues that these static analysis tools allow businesses to reduce costs while accelerating delivery of reliable embedded systems.
Chapter 1-Object Oriented Software Engineering.pptx
This document provides an introduction to software engineering and object-oriented concepts. It defines key terms like program, documentation, software characteristics. It describes various software engineering methodologies like Coad and Yourdon, Booch, Rumbaugh, and Jacobson. It also discusses object-oriented modeling, the Unified Modeling Language (UML), and compares traditional vs. object-oriented approaches.
Association Rule Mining Scheme for Software Failure Analysis
The software execution process is tracked with event logs. The event logs are used to maintain the
execution process flow in a textual log file. The log file also manages the error values and their source of classes.
The error values are used to analyze the failure of the software. The data mining methods are used to evaluate the
quality and software failure rate analysis process. The text logs are processed and data values are extracted from
the data values. The data values are mined using the machine learning methods for failure analysis.
The service error, service complaints, interaction error and crash errors are maintained under the log files.
The events and their reactions are also maintained under the log files. Software termination and execution failures
are identified using the log details. The log file parsing process is applied to extract data from the logs. The
associations rule mining methods are used to analyze the log files for failure detection process. The system uses
the Weighted Association Rule Mining (WARM) scheme to fetch failure rate in the software execution flow. The
system improves the failure rate detection accuracy in WARM model.
10 Year Impact Award Presentation - Duplicate Bug Reports Considered Harmful ...
This document describes a new automated method called SEVERIS that assists NASA test engineers in assigning severity levels to defect reports. SEVERIS uses text mining and machine learning techniques on NASA's Project and Issue Tracking System (PITS) database to predict issue severities. A case study found that SEVERIS accurately predicts severities and provides probability estimates, helping guide decision making during the severity assessment process.
Introduction to Software Engineering
This document outlines the course CSSE3113 which introduces students to software engineering concepts, principles and techniques to enable them to apply what they learn to develop software, describes the activities involved in software engineering, and explains the evaluation criteria and expectations for the course including a prohibition on plagiarism.
Presentation
This document analyzes the relationship between software bug type and the number of factors involved in failures. It finds that most bugs are caused by one or two factors, while progressively fewer bugs involve more factors. It also finds that complex "Mandelbug" software bugs fall within the range that can be tested using conventional combinatorial testing techniques. This suggests that combinatorial testing can achieve confidence levels similar to exhaustive testing for more complicated bugs.
Similar to SFSCon19 - Kristian Schwienbacher - Custom error and event tracking for Ember js apps (20)
Assignment 1 Week 2.docx1Assignment 1 Topic Selection.docx
Assignment 1 Week 2.docx1Assignment 1 Topic Selection.docx
Software engineering practices and software quality empirical research results
Software engineering practices and software quality empirical research results
IRJET- A Detailed Analysis on Windows Event Log Viewer for Faster Root Ca...
IRJET- A Detailed Analysis on Windows Event Log Viewer for Faster Root Ca...
Using Fuzzy Clustering and Software Metrics to Predict Faults in large Indust...
Using Fuzzy Clustering and Software Metrics to Predict Faults in large Indust...
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...
A Review on Software Fault Detection and Prevention Mechanism in Software Dev...
Adaptation of the technology of the static code analyzer for developing paral...
Adaptation of the technology of the static code analyzer for developing paral...
IRJET- Data Reduction in Bug Triage using Supervised Machine Learning
IRJET- Data Reduction in Bug Triage using Supervised Machine Learning
Finding Bugs, Fixing Bugs, Preventing Bugs — Exploiting Automated Tests to In...
Finding Bugs, Fixing Bugs, Preventing Bugs — Exploiting Automated Tests to In...
An Empirical Comparison of the Development History of CloudStack and Eucalyptus
An Empirical Comparison of the Development History of CloudStack and Eucalyptus
Christopher N. Bull History-Sensitive Detection of Design Flaws B ...
Christopher N. Bull History-Sensitive Detection of Design Flaws B ...
Automation in the Bug Flow - Machine Learning for Triaging and Tracing
Automation in the Bug Flow - Machine Learning for Triaging and Tracing
Embedded software static analysis_Polyspace-WhitePaper_final
Embedded software static analysis_Polyspace-WhitePaper_final
Chapter 1-Object Oriented Software Engineering.pptx
Chapter 1-Object Oriented Software Engineering.pptx
Association Rule Mining Scheme for Software Failure Analysis
Association Rule Mining Scheme for Software Failure Analysis
10 Year Impact Award Presentation - Duplicate Bug Reports Considered Harmful ...
10 Year Impact Award Presentation - Duplicate Bug Reports Considered Harmful ...
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- 1. Kristian Schwienbacher Custom error and event tracking for Ember.js apps
- 2. “Identification of Bug-Inducing Commits Based on User Activity” Kristian Schwienbacher Free University of Bolzano - Bachelor in Computer Science Bachelor Thesis
- 3. PROBLEM SOLUTION EVALUATION Comparison with the SZZ Algorithm PROBLEM STATEMENT Implementation of the System Outline DISCUSSION 01 02 03 04 Goal of the project and Requirements of the System Limitations and Scope of Application
- 5. ● Errors inside running applications must be tracked ● Interactions of users are logged in form of stack-traces ● Error-log are analyzed to determine bug-inducing commit ● Comparison with traditional bug-localization algorithm PROBLEM STATEMENT
- 6. Phase 1 SZZ Algorithm Bug-fixing commits git blame bug-introducing commits candidates ● Sliwersky, Zimmermann, Zeller (2005) ● Find bug-fixing commits ● Trace back to bug-introducing commit
- 9. PROBLEM SOLUTION Error Tracking Addon ● Catches all errors occurring during run-time ● Adds event listeners for pre-defined user events ● When error occurs, log is sent to API-endpoint ● Distributed as an open source NPM package
- 10. PROBLEM SOLUTION Measurement Server ● initial state of application repository is analyzed ○ SZZ algorithm ○ average bug-fixing time ● logging endpoint ○ analyzes stack-traces to determine bug-inducing-commit ○ GitHub Issues
- 11. EVALUATION 04
- 12. EVALUATION Data Exploration Weekday distribution of bug-introducing commits
- 14. EVALUATION Data Exploration Weekday distribution of bug-fixing commits
- 15. EVALUATION Data Exploration Bug introducing commit message analysis
- 16. CASE STUDIES 4.1
- 17. EVALUATION Case Study I ● Bug: no plans would be available when existing customer tried to upgrade one of their applications ● Fixing Commit: 92 line modifications, 7 files ● SZZ: pointed to commit made 17 days prior to bugfix ● Error Logger: bug was introduced 74 days before being fixed
- 18. DISCUSSION 05
- 19. Discussion ● The majority of the bugs do not generate any error message ● SZZ algorithm can be applied on most bugs ● Approach of this project only applicable when stack traces are generated ● When stack trace is available this approach was determined to be more accurate in the case studies
- 20. References ● T. Zimmermann J. Sliwerski and A. Zeller. When do changes induce fixes? In Proceedings of International Workshop on Mining Software Repositories, pages 24–28, Saint Louis, Missouri, USA, 2005. ● Sunghun Kim, Thomas Zimmermann, Kai Pan, and E. James Whitehead Jr. Automatic identification of bug introducing changes. In Proceedings of the 21st IEEE/ACM International Conference on Automated Software Engineering, pages 81–90, September 2006. ● Kim Herzig, Sascha Just, and Andreas Zeller. It’s not a bug, it’s a feature: How misclassification impacts bug prediction. In Proceedings of the 2013 International Conference on Software Engineering, ICSE ’13, pages 392–401, Piscataway, NJ, USA, 2013. IEEE Press ● Gema Rodríguez-Pérez, Gregorio Robles, and Jesús M González-Barahona. Reproducibility and Credibility in Empirical Software Engineering: A Case Study based on a Systematic Literature Review of the use of the SZZ algorithm. Information and Software Technology
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