A framework for adoption of machine learning in industry for software defect prediction

Machine learning algorithms are increasingly being used in a variety of application domains including software engineering. While their practical value have been outlined, demonstrated and highlighted in number of existing studies, their adoption in industry is still not widespread. The evaluations of machine learning algorithms in literature seem to focus on few attributes and mainly on predictive accuracy. On the other hand the decision space for adoption or acceptance of machine learning algorithms in industry encompasses much more factors. Companies looking to adopt such techniques want to know where such algorithms are most useful, if the new methods are reliable and cost effective. Further questions such as how much would it cost to setup, run and maintain systems based on such techniques are currently not fully investigated in the industry or in academia leading to difficulties in assessing the business case for adoption of these techniques in industry. In this paper we argue for the need of framework for adoption of machine learning in industry. We develop a framework for factors and attributes that contribute towards the decision of adoption of machine learning techniques in industry for the purpose of software defect predictions. The framework is developed in close collaboration within industry and thus provides useful insight for industry itself, academia and suppliers of tools and services.