Rakesh Rana1, Miroslaw Staron1, Jörgen Hansson1, Martin Nilsson2, Wilhelm Meding3

1Computer Science & Engineering, Chalmers | University of Gothenburg, Sweden

2Volvo Car Group, Gothenburg, Sweden

3Ericsson, Gothenburg, Sweden

rakesh.rana@gu.se

Software Defect Prediction (SDP) methods

Image 1: https://www.reliablesoft.net/how-to-become-an-expert-in-your-niche-even-if-you-are-not/

Image 2: Fenton, Norman, et al. "Predicting software defects in varying development lifecycles using Bayesian nets." Information and Software Technology 49.1 (2007): 32-43.

Image 3: Kan, Stephen H. Metrics and models in software quality engineering. Addison-Wesley Longman Publishing Co., Inc., 2002.

Image 4: http://www.codeodor.com/index.cfm/2009/11/12/Its-Not-Your-Fault-Your-Software-Sucks/3058

SDP: Methods based on Machine Learning

• Decision Trees (DTs)

• Support Vector Machines (SVMs)

• Artificial Neural Networks (ANNs)

• Bayesian Belief Networks (BNNs)

Image 1: http://www.webpages.uidaho.edu/veg_measure/Modules/Lessons/Module%202(Sampling)/2_3_Accuracy_and_bias.htm

Image 2: http://www.business2community.com/marketing/3-easy-keyword-research-tips-for-inbound-marketing-success-0215660#!bKz0G0

Image 3: http://dpss.co.riverside.ca.us/childrens-services-division/adoption-information/foster-adoptive-parent

Image 4: http://www.haitian-truth.org/treatys-tighter-adoption-rules-kick-in-for-haiti/

Objective

“How can we use the technology acceptance and

adoption models for developing framework for ML

adoption in industry and how to adapt it for software

defect prediction?”

Davis Jr, F.D., 1986. A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.

Research Process

Framework for Adoption of ML Techniques in Industry

Framework for Adoption of ML Techniques in Industry

H1: Higher levels of perceived benefits of adopting ML techniques will strongly

(and positively) affect the likelihood of their adoption.

H2: Higher levels of perceived barriers of adopting ML techniques will

strongly (and negatively) affect the likelihood of their adoption.

…

ML characteristics that affects its acceptance for SDP

Organizational characteristics that affects its

acceptance for defect prediction task

ML Adoption Framework: How it can be used?

Attribute Tool A Tool B

Predictive Accuracy 85% 82%

Auto data acquisition Yes Yes

Report generation Yes, web basedYes, multiple

format

Can handle multiple projects Yes Yes

Generate causal mapsYes, Non-

InteractiveYes, Interactive

Running time (typical project) 30min 40min

Cost of license (tool)$ 20000/

license$ 35000/ license

Maintenance cost (estimate) $ 7000 pa $ 9000 pa

…

To compare between two new tools/services

AttributeExisting

Method

New ML based

technique

Predictive Accuracy Good Very Good

Auto data acquisition Yes Yes

Report generationYes, word

documentYes, web based

Can handle multiple projects No Yes

Generate causal maps No Yes

Running time (typical

project)15min 30min

Cost of license (tool) None $ 20000/ license

Maintenance cost (estimate) $ 2000 pa $ 7000 pa

…

To evaluate new technique for SDP

ML Adoption Framework: Conclusions

Large and constantly growing amount of data is available

ML based techniques have high potential to aid companies in SDP efforts

ML adoption framework help increase our understanding of factors and

attributes relevant for industrial practitioners

ML adoption framework will be useful for

Companies,

Researchers, and

Tool vendors

ML Adoption Framework: Initial Industrial Validation

“The adoption of machine learning techniques for software

defect prediction: An initial industrial validation”

In Proceedings of “Joint Conference on Knowledge-Based Software Engineering”