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SOFT THEORY: A PRAGMATIC ALTERNATIVE TO
CONDUCT QUANTITATIVE EMPIRICAL STUDIES
Daniel Russo* & Klaas-Jan Stol
HOW TO DEAL WITH EMERGENT
PHENOMENA USING QUANTITATIVE
DATA?
ESPECIALLY IN INDUSTRY SETTINGS?
TYPICAL QUESTIONS
➤ Where should I start?
➤ How to minimise practitioners
efforts?
➤ How to design my research in
a ‘sustainable’ way?
➤ How can I investigate it in a
credible way?
➤ How can I convey
community’s discussion on
my topic?
‘SOFT’ THEORISE YOUR
FINDINGS
SOFT THEORY HARD THEORY
Context New research phenomena Well known subject matter
Goal Preliminary understanding
Confirm & Extend SE
theories
Outcome Conceptual models Causation-based model
Example
Why episodic volunteers
contribute to FLOSS projects
Software cost estimation
(COCOMO I & II)
Dinesh Batra, 2018. Agile values or plan-driven aspects: Which factor contributes more toward the success of data
warehousing, business intelligence, and analytics project development?, Journal of Systems and Software, V. 146, pp.
249-262
For example…
NOT ALL DATA ANALYSES
ARE THE SAME
Data Analysis Aim Methods Example
Descriptive
Description of a
data set
Univariate
State of Agile
(VersionOne)
Exploratory Find relationships Correlation
SW metrics & B.
indicators
Inferential Hypotesis testing Regression
Defect decrease
with code reviews
Predictive Hypotesis testing Regression
Defect prediction
in NASA’s data
Causal
Causal
relationships
Controlled
experiment
Tools decreases
bugs
Mechanistic Exact relationship
Experiments w.
determ. equations
COCOMO
CONCLUSIONS
➤ Don’t use hard theory
approaches if there is little
knowledge available
➤ Use research instruments
which provide rich insights
from little knowledge
➤ Soft theory techniques are
robust models which provide
insightful understanding of
new phenomena
➤ Be consistent with the
approach and avoid wrong
claims
QUESTIONS
daniel.russo@lero.ie

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Soft Theory: A Pragmatic Alternative to Conduct Quantitative Empirical Studies

  • 1. SOFT THEORY: A PRAGMATIC ALTERNATIVE TO CONDUCT QUANTITATIVE EMPIRICAL STUDIES Daniel Russo* & Klaas-Jan Stol
  • 2. HOW TO DEAL WITH EMERGENT PHENOMENA USING QUANTITATIVE DATA? ESPECIALLY IN INDUSTRY SETTINGS?
  • 3. TYPICAL QUESTIONS ➤ Where should I start? ➤ How to minimise practitioners efforts? ➤ How to design my research in a ‘sustainable’ way? ➤ How can I investigate it in a credible way? ➤ How can I convey community’s discussion on my topic?
  • 5. SOFT THEORY HARD THEORY Context New research phenomena Well known subject matter Goal Preliminary understanding Confirm & Extend SE theories Outcome Conceptual models Causation-based model Example Why episodic volunteers contribute to FLOSS projects Software cost estimation (COCOMO I & II)
  • 6. Dinesh Batra, 2018. Agile values or plan-driven aspects: Which factor contributes more toward the success of data warehousing, business intelligence, and analytics project development?, Journal of Systems and Software, V. 146, pp. 249-262 For example…
  • 7. NOT ALL DATA ANALYSES ARE THE SAME
  • 8.
  • 9. Data Analysis Aim Methods Example Descriptive Description of a data set Univariate State of Agile (VersionOne) Exploratory Find relationships Correlation SW metrics & B. indicators Inferential Hypotesis testing Regression Defect decrease with code reviews Predictive Hypotesis testing Regression Defect prediction in NASA’s data Causal Causal relationships Controlled experiment Tools decreases bugs Mechanistic Exact relationship Experiments w. determ. equations COCOMO
  • 10. CONCLUSIONS ➤ Don’t use hard theory approaches if there is little knowledge available ➤ Use research instruments which provide rich insights from little knowledge ➤ Soft theory techniques are robust models which provide insightful understanding of new phenomena ➤ Be consistent with the approach and avoid wrong claims