Visualizinq a Better Prototype: New Simulation Tools Enable More Affordable and Relevant Application Development
Bibliographic record
Abstract
Applications have numerous hidden costs associated with extensive reworking, and there are many development projects that fail outright, says Mitch Bishop, chief marketing officer with iRise, El Segundo, California. Much of problem comes from miscommunication during project conception. In fact, only 34% of all information technology projects are delivered on time and on budget according to Standish Group, West Yarmouth, Mass. Such waste isn't limited to private sector. The Federal Bureau of Investigation itself had to scrap a $170 million Virtual Case File project for agent desktops, as widely reported early in January, due to design flaws in application, Bishop relates. The verbal miscues during development go far beyond polite disagreements over budgets or territorial posturing. They relate to how imprecise people tend to be when attempting to translate visual, subtle, or ineffable into words. Add to that IT specialist who doesn't explain what's feasible based on what's being said and business analyst who's trying to parse specialized lingo from both parties and you've got a bad application just waiting to be hatched. It's relatively easy to describe what an application ought to do, says Carl Zetie, vice-president and analyst with Forrester Research, Cambridge, Mass. It's much harder to describe how application should function--for instance, how a trading screen should behave, he explains. Moreover, all of these business issues have been as commonplace as they are tedious and, until fairly recently, have had no easy solution. It's show me, don't tell me problem, Zetie says. Which means endless coding and recoding--just to get a prototype, never mind production model. And, at end of it all, you still might wind up with what can kindly be called, the not quite right application. Like Zetie, iRise's Bishop is in a position to know these tricks--and downfalls--of development trade. His firm has helped to launch a new genre of development tools aimed at analyst, who bridges gap between business users and IT, as opposed to developer. This is a big deal because U.S. companies, it turns out, are big believers in custom made, creating about $100 billion worth in specialized applications (versus shelfware), according to Forrester. Visualization prototyping is a rapidly expanding area attracting new firms that have slightly different approaches but all promise to help streamline customized application production. iRise, offers user interface (UI) generation capabilities; Toronto-based Sofea, provides a user modeling language (UML) approach and detailed requirements gathering capability; and Apptero, Oakland, Calif., simulates UI and business rules and also can generate web-service links to back-end systems for creating prototype environments. All offer banks new relatively inexpensive options. Addressing a common problem Many times, requirements-gathering process of is given short shrift. Poor requirements-gathering management is often a part of problem and causes project delays and additional costs, asserts Melinda Ballou, senior program director, MetaGroup, recently acquired by Gartner, Stamford, Conn. MetaGroup issued a research note on growing importance of automated requirements-gathering tools (an area, like rapid prototyping, that can streamline application development). The research concluded that their use can result in better execution of applications, which is one reason why Ballou likes Sofea's solution. We can provide user interface simulation that iRise and others provide, notes Sofea's senior vice-president of strategy Paul Smith. But we also pay careful attention to requirements-discovery-generating 'artifacts,' which are specific details about requirements written in format and language that specialists such as designers, developers, and testers understand. …
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.080 | 0.016 |
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".