Automatic Wrapping of Legacy Code and the Mediation of its Data
Bibliographic record
Abstract
Recently, Scientific and Engineering communities are employing Grid enabled software applications. To be widely adopted, Java applications in particular will require more support the integration of legacy applications [1]. Triana is an example of such an application and therefore, here, it is used to prototype the development of our plug-in legacy code support. C programming is the language used extensively for scientific applications and consequently, the requirement for a method to incorporate software and applications in C is of prime importance. In the future, we will include support for C++ and other languages. The software we are developing consists of three components: Jacaw [2], a datamediation interface and a compilation wizard Jacaw: Jacaw is used to generate a Java wrapper class that is used to call functions from the C library. The wrapper class uses JNI (Java Native Interface) to interface with the C library (which is compiled as a shared library) [3]. Figure 1: JACAW structure Data Mediation Interface: The data mediation interface automates the process of mediating the data types between the Java classes and the C function calls. This is accomplished by providing a graphical user interface (GUI) that takes the user through a set of steps to select the parameters for the wrapped function from an input class and then to initialize and set the data for the output class. For example, a Triana unit takes a Java data-type class as its input and outputs a Java data-type class (which can be different to the output class). Briefly, the GUI is divided into three sections. The first allows the user to choose the mapping of the Java instance variables (via Java-bean getParameter() type function calls) to the arguments of the C function call. The second stage allows the user to mediate the data returned from the C function to the Java code. Lastly, the user can then mediate any other data between the input Java class and the output Java class which may not be needed by the C function. The allows the input object to be decomposed and then reconstructed after the native call allowing the user to integrate the C function call without writing any Java code. Compilation Wizard: This module is a user friendly interface that complements JACAW. It enables compilation and creation of a shared library from legacy codes in order to incorporate applications written in C into Triana. The Wizard initially detects the operating system and instructs the user to select C source file(s), libraries, output file and takes the user through steps in selecting preferred C compiler to create. The following figure illustrates overall relation between Triana units [4] and use of legacy application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".