Name
Abstract | ||
|---|---|---|
One of the most important challenges in empirical software engineering today is to better integrate empirical studies with
decision support, and to collect appropriate data and experiments. The required steps are to identify the information needed,
to collect appropriate studies, and to (objectively) aggregate (i.e., summarize) their results. To be able to make informed
decisions on introducing, changing, or evolving technologies and processes in practice as well as research, these decisions
have to be based on aggregated trustable (i.e., corroborated) evidence and statements. The benefits of such an approach include
reducing the risk of introducing / changing technologies (from industrial point of view), and that it is possible to identify
evidence gaps (from research point of view).
|
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/978-3-540-71301-2_4 | Empirical Software Engineering Issues |
Keywords | Field | DocType |
empirical evidence,empirical study,empirical software engineering,decision support | Data science,Econometrics,Empirical evidence,Computer science,Decision support system,Empirical process (process control model),Empirical research | Conference |
Volume | ISSN | Citations |
4336 | 0302-9743 | 2 |
PageRank | References | Authors |
0.36 | 1 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcus Ciolkowski | 1 | 355 | 25.44 |