Sofya: Supporting Rapid Development of Dynamic Program Analyses for Java

Dynamic analysis is an increasingly important means of supporting software validation and maintenance. To date, developers of dynamic analyses have used low-level instrumentation and debug interfaces to realize their analyses. Many dynamic analyses, however, share multiple common high-level requirements, e.g., capture of program data state as well as events, and efficient and accurate event capture in the presence of threading. We present SOFYA – an infra-structure designed to provide high-level, efficient, concurrency-aware support for building analyses that reason about rich observations of program data and events. It provides a layered, modular architecture, which has been successfully used to rapidly develop and evaluate a variety of demanding dynamic program analyses. In this paper, we describe the SOFYA framework, the challenges it addresses, and survey several such analyses.

The South Omaha Community Scholarship Program: A Qualitative Study of Persistence of Hispanic Students at Bellevue University

The purpose of this case study was to determine the impact of the South Omaha Community Scholarship Program on the persistence of the Hispanic students who participated. Previous research on Hispanic student persistence has focused on the reasons why students do not persist and more recent research has been conducted on programs and retention efforts, colleges and universities are implementing on their campuses. This study researched a specific program, The South Omaha Community Scholarship Program, designed to provide financial, academic and other needed resources to help Hispanic students persist to graduation. The researcher believes this study was important because it provided an overview of how the South Omaha Community Scholarship Program is affecting students both on campus and in their community. Eight interviews were conducted, with eligible students, in person. Students eligible for the study were current students or recent graduates of the South Omaha Community Scholarship Program and had attained at least junior or senior status as of the fall of 2009, as defined by Bellevue University. Research questions were based on the four components of the program and the affect the program had on the student’s life, outside of Bellevue University. The four components of the program were: financial aid, academic advising, the scholarship aid, and the Professional Enrichment Program. The results of the study were broken into five components with an additional section that provided other themes that were derived from the interviews. The five components were: (a) financial aid counseling, (b) academic advising, (c) scholarship aid, (d) Professional Enrichment Program, and (e) the South Omaha Community Scholarship Program beyond Bellevue University. Other themes that were derived from the interviews were: class format, deciding on a college, higher education class, campus resources, and a sense of community on-campus. The research found that the scholarship, provided by the South Omaha Community Scholarship Program, was the primary motivating factor for students to attend Bellevue University and persist in college. The interviewed students also commented on how the scholarship had given them the opportunity to attend college, even though that opportunity had seemed out of reach. The interviewed students also commented on their academic advising experience, campus resources, and feeling a sense of community on-campus as other campus related areas that were affected by the South Omaha Community Scholarship Program. Finally, students provided examples of how the South Omaha Community Scholarship Program impacted their connection to their South Omaha community through volunteer and employment opportunities. Adviser: Richard Hoover

JVM-based Techniques for Improving Java Observability

Observability measures the support of computer systems to accurately capture, analyze, and present (collectively observe) the internal information about the systems. Observability frameworks play important roles for program understanding, troubleshooting, performance diagnosis, and optimizations. However, traditional solutions are either expensive or coarse-grained, consequently compromising their utility in accommodating today’s increasingly complex software systems. New solutions are emerging for VM-based languages due to the full control language VMs have over program executions. Existing such solutions, nonetheless, still lack flexibility, have high overhead, or provide limited context information for developing powerful dynamic analyses. In this thesis, we present a VM-based infrastructure, called marker tracing framework (MTF), to address the deficiencies in the existing solutions for providing better observability for VM-based languages. MTF serves as a solid foundation for implementing fine-grained low-overhead program instrumentation. Specifically, MTF allows analysis clients to: 1) define custom events with rich semantics ; 2) specify precisely the program locations where the events should trigger; and 3) adaptively enable/disable the instrumentation at runtime. In addition, MTF-based analysis clients are more powerful by having access to all information available to the VM. To demonstrate the utility and effectiveness of MTF, we present two analysis clients: 1) dynamic typestate analysis with adaptive online program analysis (AOPA); and 2) selective probabilistic calling context analysis (SPCC). In addition, we evaluate the runtime performance of MTF and the typestate client with the DaCapo benchmarks. The results show that: 1) MTF has acceptable runtime overhead when tracing moderate numbers of marker events; and 2) AOPA is highly effective in reducing the event frequency for the dynamic typestate analysis; and 3) language VMs can be exploited to offer greater observability.