Negative results for software effort estimation

Negative results for software effort estimation More than half the literature on software effort estimation (SEE) focuses on comparisons of new estimation methods. Surprisingly, there are no studies comparing state of the art latest methods with decades-old approaches. Accordingly, this paper takes five steps to check if new SEE methods generated better estimates than older methods. Firstly, collect effort estimation methods ranging from “classical” COCOMO (parametric estimation over a pre-determined set of attributes) to “modern” (reasoning via analogy using spectral-based clustering plus instance and feature selection, and a recent “baseline method” proposed in ACM Transactions on Software Engineering). Secondly, catalog the list of objections that lead to the development of post-COCOMO estimation methods. Thirdly, characterize each of those objections as a comparison between newer and older estimation methods. Fourthly, using four COCOMO-style data sets (from 1991, 2000, 2005, 2010) and run those comparisons experiments. Fifthly, compare the performance of the different estimators using a Scott-Knott procedure using (i) the A12 effect size to rule out “small” differences and (ii) a 99 % confident bootstrap procedure to check for statistically different groupings of treatments. The major negative result of this paper is that for the COCOMO data sets, nothing we studied did any better than Boehms original procedure. Hence, we conclude that when COCOMO-style attributes are available, we strongly recommend (i) using that data and (ii) use COCOMO to generate predictions. We say this since the experiments of this paper show that, at least for effort estimation, how data is collected is more important than what learner is applied to that data. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Empirical Software Engineering Springer Journals

Negative results for software effort estimation

Loading next page...
 
/lp/springer_journal/negative-results-for-software-effort-estimation-f0knXmSgrA
Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Computer Science; Software Engineering/Programming and Operating Systems; Programming Languages, Compilers, Interpreters
ISSN
1382-3256
eISSN
1573-7616
D.O.I.
10.1007/s10664-016-9472-2
Publisher site
See Article on Publisher Site

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial