MiSFIT: Mining Software Fault Information and Types

As software becomes more important to society, the number, age, and complexity of systems grow. Software organizations require continuous process improvement to maintain the reliability, security, and quality of these software systems. Software organizations can utilize data from manual fault classification to meet their process improvement needs, but organizations lack the expertise or resources to implement them correctly. This dissertation addresses the need for the automation of software fault classification. Validation results show that automated fault classification, as implemented in the MiSFIT tool, can group faults of similar nature. The resulting classifications result in good agreement for common software faults with no manual effort. To evaluate the method and tool, I develop and apply an extended change taxonomy to classify the source code changes that repaired software faults from an open source project. MiSFIT clusters the faults based on the changes. I manually inspect a random sample of faults from each cluster to validate the results. The automatically classified faults are used to analyze the evolution of a software application over seven major releases. The contributions of this dissertation are an extended change taxonomy for software fault analysis, a method to cluster faults by the syntax of the repair, empirical evidence that fault distribution varies according to the purpose of the module, and the identification of project-specific trends from the analysis of the changes

Toward a Learned Project-Specific Fault Taxonomy: Application of Software Analytics A Position Paper

Abstract-This position paper argues that fault classification provides vital information for software analytics, and that machine learning techniques such as clustering can be applied to learn a project-(or organization-) specific fault taxonomy. Anecdotal evidence of this position is presented as well as possible areas of research for moving toward the posited goal

Comparison of MRI and CT for detection of acute intracerebral hemorrhage.

CONTEXT: Noncontrast computed tomography (CT) is the standard brain imaging study for the initial evaluation of patients with acute stroke symptoms. Multimodal magnetic resonance imaging (MRI) has been proposed as an alternative to CT in the emergency stroke setting. However, the accuracy of MRI relative to CT for the detection of hyperacute intracerebral hemorrhage has not been demonstrated. OBJECTIVE: To compare the accuracy of MRI and CT for detection of acute intracerebral hemorrhage in patients presenting with acute focal stroke symptoms. DESIGN, SETTING, AND PATIENTS: A prospective, multicenter study was performed at 2 stroke centers (UCLA Medical Center and Suburban Hospital, Bethesda, Md), between October 2000 and February 2003. Patients presenting with focal stroke symptoms within 6 hours of onset underwent brain MRI followed by noncontrast CT. MAIN OUTCOME MEASURES: Acute intracerebral hemorrhage and any intracerebral hemorrhage diagnosed on gradient recalled echo (GRE) MRI and CT scans by a consensus of 4 blinded readers. RESULTS: The study was stopped early, after 200 patients were enrolled, when it became apparent at the time of an unplanned interim analysis that MRI was detecting cases of hemorrhagic transformation not detected by CT. For the diagnosis of any hemorrhage, MRI was positive in 71 patients with CT positive in 29 (P CONCLUSION: MRI may be as accurate as CT for the detection of acute hemorrhage in patients presenting with acute focal stroke symptoms and is more accurate than CT for the detection of chronic intracerebral hemorrhage

TIC 172900988: A Transiting Circumbinary Planet Detected in One Sector of TESS Data

International audienc