Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Lang Gou

dblp:54/3621 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software maintenance and evolution · 100%

Topics — the 1 heaviest of 1, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software maintenance and evolution
software process improvement
0.012006
BSR: a statistic-based approach for establishing and refining software process performance baseline · ICSE 2006

Methods — techniques the papers use, named apart from their topics

statistical process control · 0.1baseline refinement · 0.1
YearPublicationVenuePosition
2006 BSR: a statistic-based approach for establishing and refining software process performance baseline
abstract
High-level process management is quantitative management. The Process Performance Baseline (PPB) of process or subprocess under statistical management is the most important concept. It is the basis of process control and improvement. The existing methods for establishing process baseline are too coarse-grained or have some limitation, which lead to inaccurate or ineffective quantitative management. In this paper, we propose an approach called BSR (Baseline-Statistic-Refinement) for establishing and refining software process performance baseline, and present the experience result to validate its effectiveness for quantitative process management.
Qing Wang 0001, Nan Jiang 0001, Lang Gou, Mingshu Li 0001, Yongji Wang 0002
ICSE3