Design of Study	Recognising Need for a Study	Defining a Research Question	Data Access and Gathering + Data Cleanup	Threat Identification	Validation of Results	Interpretation of Results + Transfer of Results	Getting Industry Commitment
Alignment with Business Goals	1. Study design must fit company's reality. 2. ROI -> this is all that matters in industry. 3. You need to be open to adapt to company needs even if out of your expertise. 4. Compromise: industry needs vs. research needs.	1. The relationship of empircal studies of software with long term business goals. Study must be of interest to business. 2. How to 'sell' an exploratory study e.g., survey state of practice to possible participants.	1. A research question should be aligned with at least one business goal. 2. If you have a research question find a business goal that answering the question meets the goal. 3. RQ should describe feasible business challenges.	1. Sell the study better; get more data.	1. Observe business restrictions. 2. Risk management -> increased chance of ROI -> industrial trust. 3. Can you identify a threat and its solution? 4. Industry focuses on survival not clever and fancy.	1. Include observations regarding business. 2. Find a process being repeated where method can be changed.	1. What principles might be deduced to argue on behalf of interpretation? 2. ARTC Methods need to be Adoptable, repeatable, teachable, costable/use.
Stakeholder Motivation & Commitment	1. Establishes language of doing and real problem. 2. Necessary to avoid Validity threats. 3. Manageable study length especially if for a PhD. 4. Are the primary, secondary and tertiary stakeholders; are the latter two being ignored?	1. Assess company's Reality. 2. See comments on column on right	1. Deal with real problem. Learn about target and their technology in market - identify their needs. See my techico commercial drivers.	1. Danger of missing values.	1. Will the company collapse before the study ends? Personnel change in company can harm study.		1. KISS -> keep it simple for solutions for industry.	1. Technico-business drivers: how to relate business needs to research.
Challenges, Barriers, etc.	1. Consider the difficulty factors. 2. When reasearch should be done by outsiders? 3. Technology still mentioned as an issue year after year; who submits a paper next year on how to overcome?	1. Trustability short time for assessments. 2. First thing to do with a company build trust.	1. To find the real problem.	1. Finding the right person to talk to. 2. So much work! Often resources are lacking. 3. Find non-intrusive methods of data collection; use GQM! 4. Dealing with confidential data. 5. Motivating participants. 6. How much does using the correct statistical procedures matter?	1. Tradeoff analysis. 2. Building 'communities of trust'. 3. To what extent and in way should researchers' predispositions be acknowledged? 4. Recognition of conventional change management scenarios and practices. 5. Vocabulary: are we talking about the same thing?	1. Need to conduct industry trials that are non-intrusive. 2. May need to find industry to do trials. 3. Hard to control variables in replication studies.	1. How will an experienced practitioner see results: too complex, trivial, threatening. 2. How to publish results in academia. 3. How to consider confidential information? 4. How to foster tech transfer to professional, students? 5. Problems may be complext but solutions must be simple. 6. 'Good enough' in inudstry is perfect, more than 'good enough' is too costly.	1. Learning industry's needs.
Tips, Lessons Learnt, Solutions	1. Cannot capture all context; that's ok! 2. General vs. particular; obstrusive vs. non-obstrusive. 3. Older, experienced researchers can often see a problem.	1. Fragment the study in more platable parts.	1. Take into conideration technology is volatile.	1. If you have a result that is better and a respected champion that uses it. 2. How to explain the area of interest to the interviewee without influencing the answers. 3. How to record, make them available and cite interviews without transcribing. 4. Recognise it will be hard to predict adoption.	1. Continuous industrial communication and feedback. 2. Have more than one 'champion' in company.		1. Add Bayesian stuff to method to make it suitable for academic publications.	1. Try research disclosure sessions; What we are doing, why it might be useful.
Feedback to/from Stakeholders	1. Feasible and real design.	1. Problem identification in industry, observations, analyses as from keynotes. 2. Assessment reports and issues/challenegs perspectives.	1. When framing research questions , what ethical issues must be considered. Frame questions if possible in terms of industrial outcome.	1. Provide a complete set of reports also produce reports written for new research.	1. Be upfront about threats of study to industry (from paper this morning. 2. Should contextualise findings.	1. Independent industrial integration; they use 'it' even when you leave.	1. Should be easy to understand. 2. ARTC.
Industry Setting	1. Context defines study not the opposite. 2. Industry settings should capture context and understand its effects.	1. Q==F(P;C) Question, problem, effectiveness, context.		1. Try to make as much data operational as possible (with permission).	1. Recognise your target may already be successful. 2. The worst threat is not matching industry setting. 3. Researcher's pet idea might not always be appropriate.		1. Find a company committed to R&d. 2. How to decide what is proprietary research results and what is stakeholder's.
Principles and Fundamnetals	1. See ISERN Related reports. 2. How about a 3. handbook of SE esearch processes? 4. Epistimiology in SE; research needs attention 5. Pizza framework (see Runkel and McGrath 1972) helpful particularly for teaching. 6. Systematic literature reviews; evidence-based SE (not much here)	1. For those seeking venture capital isn't the first round seeking money for operational study	1. Problems in the real world should lead to a theory which will help to identify RQs.			1. Research Q's should be assured through different types of studies/ methods.	1. Study results should be interpreted in context of a working" theory to add to knowledge base.
Tool Support	1. Tool support for study design is a need. 2. I don't know of any tool to help study design that would help. 3. Can you use existing tools?			1. Tool support for data gathering and cleanup is a need. 2. Essential in order to have data analysed on time. 3. Any experience performing interviews involving text chat tools like skype.			1. Prepositions of case study dots + results.