Supporting Electronic Commerce of Software Products through Pay-Per-Use Rental of Downloadable Tools
Bibliographic record
Abstract
The pervasiveness of Internet connectivity and the wide diffusion of Java-capable browsers foster innovative techniques for software distribution. In this chapter, we propose a new model for the electronic commerce of software tools based on a pay-per-use rental policy. Pay-per-use rental of downloadable tools is the natural exploitation of Java applets that can be transferred on demand to the user’s machine and executed dynamically inside a browser. While software rental is not a new idea (Flamnia and McCandless, 1996), at present no example of a standard pay-per-use rental mechanism for downloadable software tools exists. This approach benefits from the advantages of central management of tools and zero maintenance for users typical of Java applets, together with a new way to pay for their use. Software rental presents several advantages to producers and users. Pay-per-use rental is particularly suited to Web-based applications, because they are offered to a very heterogeneous and dynamic user population (Bakos and Brynjolfson, 1997). This chapter describes advantages and issues related to pay-per-use, and explains how to add it to Web-based systems, by presenting the example of pay-per-use integration in WebMetrics, a Web-based system providing distributed collection, management, and analysis of source code metrics. This chapter is organized as follows. Section 2 discusses tools-on-demand. Section 3 presents the role of pay-per-use. Section 4 introduces WebMetrics, our prototype pay-per-use application. Section 5 describes the architecture of WebMetrics. Section 6 presents a list of open issues. Section 7 draws some conclusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".