Combining hardware and software instrumentation to classify program executions

Several research efforts have studied ways to infer properties of software systems from program spectra gathered from the running systems, usually with software-level instrumentation. While these efforts appear to produce accurate classifications, detailed understanding of their costs and potential cost-benefit tradeoffs is lacking. In this work we present a hybrid instrumentation approach which uses hardware performance counters to gather program spectra at very low cost. This underlying data is further augmented with data captured by minimal amounts of software-level instrumentation. We also evaluate this hybrid approach by comparing it to other existing approaches. We conclude that these hybrid spectra can reliably distinguish failed executions from successful executions at a fraction of the runtime overhead cost of using software-based execution data

Semi Automated Partial Credit Grading of Programming Assignments

The grading of student programs is a time consuming process. As class sizes continue to grow, especially in entry level courses, manually grading student programs has become an even more daunting challenge. Increasing the difficulty of grading is the needs of graphical and interactive programs such as those used as part of the UNH Computer Science curriculum (and various textbooks). There are existing tools that support the grading of introductory programming assignments (TAME and Web-CAT). There are also frameworks that can be used to test student code (JUnit, Tester, and TestNG). While these programs and frameworks are helpful, they have little or no no support for programs that use real data structures or that have interactive or graphical features. In addition, the automated tests in all these tools provide only “all or nothing” evaluation. This is a significant limitation in many circumstances. Moreover, there is little or no support for dynamic alteration of grading criteria, which means that refactoring of test classes after deployment is not easily done. Our goal is to create a framework that can address these weaknesses. This framework needs to: 1. Support assignments that have interactive and graphical components. 2. Handle data structures in student programs such as lists, stacks, trees, and hash tables. 3. Be able to assign partial credit automatically when the instructor can predict errors in advance. 4. Provide additional answer clustering information to help graders identify and assign consistent partial credit for incorrect output that was not predefined. Most importantly, these tools, collectively called RPM (short for Rapid Program Management), should interface effectively with our current grading support framework without requiring large amounts of rewriting or refactoring of test code

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

Commensal observing with the Allen Telescope array: software command and control

The Allen Telescope Array (ATA) is a Large-Number-Small-Diameter radio telescope array currently with 42 individual antennas and 5 independent back-end science systems (2 imaging FX correlators and 3 time domain beam formers) located at the Hat Creek Radio Observatory (HCRO). The goal of the ATA is to run multiple back-ends simultaneously, supporting multiple science projects commensally. The primary software control systems are based on a combination of Java, JRuby and Ruby on Rails. The primary control API is simplified to provide easy integration with new back-end systems while the lower layers of the software stack are handled by a master observing system. Scheduling observations for the ATA is based on finding a union between the science needs of multiple projects and automatically determining an efficient path to operating the various sub-components to meet those needs. When completed, the ATA is expected to be a world-class radio telescope, combining dedicated SETI projects with numerous radio astronomy science projects.Comment: SPIE Conference Proceedings, Software and Cyberinfrastructure for Astronomy, Nicole M. Radziwill; Alan Bridger, Editors, 77400Z, Vol 774

Sawja: Static Analysis Workshop for Java

Static analysis is a powerful technique for automatic verification of programs but raises major engineering challenges when developing a full-fledged analyzer for a realistic language such as Java. This paper describes the Sawja library: a static analysis framework fully compliant with Java 6 which provides OCaml modules for efficiently manipulating Java bytecode programs. We present the main features of the library, including (i) efficient functional data-structures for representing program with implicit sharing and lazy parsing, (ii) an intermediate stack-less representation, and (iii) fast computation and manipulation of complete programs

The Dark Energy Survey Data Management System

The Dark Energy Survey collaboration will study cosmic acceleration with a 5000 deg2 griZY survey in the southern sky over 525 nights from 2011-2016. The DES data management (DESDM) system will be used to process and archive these data and the resulting science ready data products. The DESDM system consists of an integrated archive, a processing framework, an ensemble of astronomy codes and a data access framework. We are developing the DESDM system for operation in the high performance computing (HPC) environments at NCSA and Fermilab. Operating the DESDM system in an HPC environment offers both speed and flexibility. We will employ it for our regular nightly processing needs, and for more compute-intensive tasks such as large scale image coaddition campaigns, extraction of weak lensing shear from the full survey dataset, and massive seasonal reprocessing of the DES data. Data products will be available to the Collaboration and later to the public through a virtual-observatory compatible web portal. Our approach leverages investments in publicly available HPC systems, greatly reducing hardware and maintenance costs to the project, which must deploy and maintain only the storage, database platforms and orchestration and web portal nodes that are specific to DESDM. In Fall 2007, we tested the current DESDM system on both simulated and real survey data. We used Teragrid to process 10 simulated DES nights (3TB of raw data), ingesting and calibrating approximately 250 million objects into the DES Archive database. We also used DESDM to process and calibrate over 50 nights of survey data acquired with the Mosaic2 camera. Comparison to truth tables in the case of the simulated data and internal crosschecks in the case of the real data indicate that astrometric and photometric data quality is excellent.Comment: To be published in the proceedings of the SPIE conference on Astronomical Instrumentation (held in Marseille in June 2008). This preprint is made available with the permission of SPIE. Further information together with preprint containing full quality images is available at http://desweb.cosmology.uiuc.edu/wik