2022 · 10 citations · 2 references
ComputingComputer ScienceData MiningMachine LearningSoftware EngineeringSoftware DocumentationSoftware MaintenanceSoftware MetricsSoftware Project ManagementAccuracyDocumentation MiningRepository MiningSoftware Repository MiningSource Code MetricsTechnical DebtTechnical Debt Assessment
The advancement of artificial intelligence and the implementation of machine learning capabilities in programming languages such as Python, along with cloud services, allow researchers to apply methods to cluster and predict behaviors and patterns in software engineering data. On the other hand, these methods need a large amount of data in order to work with high accuracy in different contexts. This paper introduces Sonarlizer Xplorer: a tool that captures a large number of technical debt items and code metrics from public GitHub projects. Sonarlizer Xplorer is composed of two sub-tools. The first is Github Xplorer, responsible for mining public Github repositories from an initial project. The second is Sonarlizer, responsible for taking projects and analyzing them using SonarQube. We used the tool over four months, collecting technical debt items and code metrics on almost 46,000 public Java projects. In addition, we mined over 57 million repositories and 4 million users.
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Using Static Analysis to Find Bugs
Nathaniel Ayewah, William Pugh, David Hovemeyer et al. · IEEE Software · 2008 · 522 citations
Computing, Computer Science, Secure Software Development +14
The role of Artificial Intelligence in Software Engineering
Mark Harman · 2012 · 98 citations