Analysis of test log information through interactive visualizations

Castro, Diego; Schots, Marcelo

doi:10.1145/3196321.3196345

Cited by 8 publications

(6 citation statements)

References 17 publications

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“…practices among GitHub contributors [41] and Mazinanian study the adoption of lamba expressions in a large sample of 2,000 Java projects, also ordered by stars [10]. Finally, Castro and Schots use GitHub stars as cut-off criterion to select projects and then analyze logging information to propose a visualization tool [14].…”

Section: Related Workmentioning

confidence: 99%

“…sample of 2,000 Java projects, also ordered by stars [10]. Finally, Castro and Schots use GitHub stars as cut-off criterion to select projects and then analyze logging information to propose a visualization tool [14].…”

Section: Threats To Validitymentioning

confidence: 99%

“…Criteria for Selecting GitHub Projects: Stars are often used by researchers to select GitHub projects for empirical studies in software engineering [7][8][9][10][11][12][13][14]. For example, in a previous study, we use the top-5,000…”

Section: Growth Patterns Characterizationmentioning

confidence: 99%

“…Therefore, this initial survey confirms the importance of GitHub stars to practitioners, when compared to forks and watchers. Additionally, stars are often used by researchers to select GitHub projects for empirical studies in software engineering [7][8][9][10][11][12][13][14]. Therefore, a throughout analysis of starring practices can shed light on the properties and risks involved in this selection.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

Borges¹,

Valente²

2018

Journal of Systems and Software

218

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Besides a git-based version control system, GitHub integrates several social coding features. Particularly, GitHub users can star a repository, presumably to manifest interest or satisfaction with an open source project. However, the real and practical meaning of starring a project was never the subject of an in-depth and well-founded empirical investigation. Therefore, we provide in this paper a throughout study on the meaning, characteristics, and dynamic growth of GitHub stars. First, by surveying 791 developers, we report that three out of four developers consider the number of stars before using or contributing to a GitHub project. Then, we report a quantitative analysis on the characteristics of the top-5,000 most starred GitHub repositories. We propose four patterns to describe stars growth, which are derived after clustering the time series representing the number of stars of the studied repositories; we also reveal the perception of 115 developers about these growth patterns. To conclude, we provide a list of recommendations to open source project managers (e.g., on the importance of social media promotion) and to GitHub users and Software Engineering researchers (e.g., on the risks faced when selecting projects by GitHub stars).

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Section: Related Workmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

Section: Growth Patterns Characterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

Borges¹,

Valente²

2018

Journal of Systems and Software

218

View full text Add to dashboard Cite

show abstract

“…Visual clues such as color, shape, and metaphors ease the cognitive load of understanding software systems [7] [40]. Numerous research has been proposed and various tools have been built to visualize different aspects of software systems, such as static program structures [11] [17] [21], dynamic program executions [8][14] [16][29], software evolution [6] [9], and debugging results [12].…”

Section: Introductionmentioning

confidence: 99%

ViBERT: Visual Behavior Regression Testing

Zhao¹,

Chen²,

Zhang³

et al. 2020

JVLC

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Regression testing is a type of software testing that aims at identifying faults caused by code changes. Regression testing is important especially during software evolution and maintenance. As developers integrate programs or make updates to a software system, they need to make sure the changes do not adversely affect other parts of the system. Using dynamic analysis, behavioral regression testing (BERT) is one of the techniques proposed to solve the problem by re-executing test cases that target the affected area. It compares the behavior of a program before and after the changes upon certain test cases. This paper proposes Visual BEhavioral Regression Testing (ViBERT), a visualization approach to comparing the behavioral differences between the new and old versions of a program in regression testing. We build a prototype called SoftLink, a visual environment that shows correlation/difference between two versions of a program behavior. SoftLink displays call graphs of two executions on angled parallel planes in a 3D space, and constructs correlations between them. It provides developers with an intuitive interpretation of the testing results. A case study is presented.

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Landscape of Automated Log Analysis: A Systematic Literature Review and Mapping Study

Korzeniowski

Goczyła

2022

IEEE Access

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Logging is a common practice in software engineering to provide insights into working systems. The main uses of log files have always been failure identification and root cause analysis. In recent years, novel applications of logging have emerged that benefit from automated analysis of log files, for example, real-time monitoring of system health, understanding users' behavior, and extracting domain knowledge. Although nearly every software system produces log files, the biggest challenge in log analysis is the lack of a common standard for both the content and format of log data. This paper provides a systematic review of recent literature (covering the period between 2000 and June 2021, concentrating primarily on the last five years of this period) related to automated log analysis. Our contribution is threefold: we present an overview of various research areas in the field; we identify different types of log files that are used in research, and we systematize the content of log files. We believe that this paper serves as a valuable starting point for new researchers in the field, as well as an interesting overview for those looking for other ways of utilizing log information.

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Analysis of test log information through interactive visualizations

Cited by 8 publications

References 17 publications

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

What’s in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform

ViBERT: Visual Behavior Regression Testing

Landscape of Automated Log Analysis: A Systematic Literature Review and Mapping Study

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