Name
Abstract | ||
|---|---|---|
Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/s10515-013-0123-1 | Autom. Softw. Eng. |
Keywords | Field | DocType |
Data mining,Evaluation,Machine learning | Systems engineering,Computer science,Software system,Artificial intelligence,Software development,Software requirements,Software engineering,Component-based software engineering,Software construction,Software verification and validation,Software requirements specification,Machine learning,Software framework | Journal |
Volume | Issue | ISSN |
21 | 1 | 0928-8910 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niklas Lavesson | 1 | 148 | 21.83 |
| Veselka Boeva | 2 | 104 | 21.59 |
| Elena Tsiporkova | 3 | 187 | 30.23 |
| Paul Davidsson | 4 | 315 | 53.19 |