Analyzing software data: after the gold rush (a goldfish-bowl panel)
Tim Menzies, Christian Bird, Thomas Zimmermann · 2014
Over the past few years, the volume and types of data related to software engineering has grown at an unprecedented rate and shows no sign of slowing. This turn of events has led to a veritable gold rush, as researchers attempt to mine raw data and extract nuggets of insight. A very real danger is that the landscape may become a Wild West where inexperienced software "cowboys" sell hastily generated models to unsophisticated business users, without any concern for best or safe practices. Given the current enthusiasm for data analysis in software engineering, it is time to review how we using those techniques and can we use them better. While there may be no single best "right" way to analyze software data, there are many wrong ways. As data techniques mature, we need to move to a new era where data scientists understand and share the strengths and drawbacks of the many methods that might be deployed in industry. In this highly interactive panel skilled practitioners and academics can (a) broadcast their insights and (b) hear the issues of newcomers in this field.