What are the major questions in software engineering that researchers should be trying to answer?
A high level question whose answer is likely to involve life, the universe, and everything is: What is the most cost-effective way to build software systems?
Viewing software engineering research as an attempt to find the answer to a big question mirrors physicists quest for a Grand Unified Theory of how the Universe works.
Physicists have the luxury of studying the Universe at their own convenience, the Universe does not need their input to do a better job.
Software engineering is not like physics. Once a software system has been built, the resources have been invested, and there is no reason to recreate it using a more cost-effective approach (the zero cost of software duplication means that manufacturing cost is the cost of the first version).
Designing and researching new ways of building software systems may be great fun, but the time and money needed to run the realistic experiments needed to evaluate their effectiveness is such that they are unlikely to be run. Searching for more cost-effective software development techniques by paying to run the realistic experiments needed to evaluate them, and waiting for the results to become available, is going to be expensive and time-consuming. A theory is proposed, experiments are run, results are analysed; rinse and repeat until a good-enough cost-effective technique is found. One iteration will take many years, and this iterative process is likely to take many decades.
Very many software systems are being built and maintained, and each of these is an experiment. Data from these ‘experiments’ provides a cost-effective approach to improving existing software engineering practices by studying the existing practices to figure out how they work (or don’t work).
Given the volume of ongoing software development, most of the payback from any research investment is likely to occur in the near future, not decades from now; the evidence shows that source code has a short and lonely existence. Investing for a payback that might occur 30-years from now makes no sense; researchers I talk to often use this time-frame when I ask them about the benefits of their research, i.e., just before they are about to retire. Investing in software engineering research only makes economic sense when it is focused on questions that are expected to start providing payback in, say, 3-5 years.
Who is going to base their research on existing industry practices?
Researching existing practices often involves dealing with people issues, and many researchers in computing departments are not that interested in the people side of software engineering, or rather they are more interested in the computer side.
Algorithm oriented is how I would describe researchers who claim to be studying software engineering. I am frequently told about the potential for huge benefits from the discovery of more efficient algorithms. For many applications, algorithms are now commodities, i.e., they are good enough. Those with a career commitment to studying algorithms have a blinkered view of the likely benefits of their work (most of those I have seen are doing studying incremental improvements, and are very unlikely to make a major break through).
The number of researchers studying what professional developers do, with an aim to improving it, is very small (I am excluding the growing number of fake researchers doing surveys). While I hope there will be a significant growth in numbers, I’m not holding my breadth (at least in the short term; as for the long term, Planck’s experience with quantum mechanics was: “Science advances one funeral at a time”).