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Empirically Assessing End User Software Engineering Techniques

Empirically Assessing End User Software Engineering Techniques. Gregg Rothermel Department of Computer Science and Engineering University of Nebraska -- Lincoln. Questions Addressed. How can we use empirical studies to better understand issues/approaches in end user SE?

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Empirically Assessing End User Software Engineering Techniques

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  1. Empirically Assessing End User Software Engineering Techniques Gregg Rothermel Department of Computer Science and Engineering University of Nebraska -- Lincoln

  2. Questions Addressed • How can we use empirical studies to better understand issues/approaches in end user SE? • What are some of the problems empiricists working on end-user SE face? • What are some of the opportunities for software engineering researchers working in this area?

  3. Outline • Background on empirical approaches • Empiricism in the end-user SE context • Problems for empiricism in end-user SE • Conclusion

  4. Outline • Background on empirical approaches • Empiricism in the end-user SE context • Problems for empiricism in end-user SE • Conclusion

  5. Empirical Approaches: Types • Survey – interviews or questionnaires • Controlled Experiment - in the laboratory, involves manipulation of variables • Case Study - observational, often in-situ

  6. Empirical Approaches: Surveys • Pose questions via interviews or questionnaires • Process: select variables and choose sample, frame questions that relate to variables, collect data, analyze and generalize from data • Uses: descriptive (assert characteristics), explanatory (assess why), exploratory (pre-study) Resource: E. Babbie,Survey Research Methods, Wadsworth, 1990

  7. Empirical Approaches:Controlled Experiments • Manipulate independent variables and measure effects on dependent variables • Requires randomization over subjects and objects (partial exception: quasi-experiments) • Relies on controlled environment (fix or sample over factors not being manipulated) • Often involves a baseline (control group) • Supports use of statistical analyses Resource: Wohlin et al.,Experimentation in Software Engineering,Kluwer, 2000

  8. Empirical Approaches: Case Studies • Study a phenomenon (process, technique, device) in a specific setting • Can involve comparisons between projects • Less control, randomization, and replicability • Easier to plan than controlled experiments • Uses include • larger investigations such as longitudinal or industrial Resource: R. K. Yin,Case Study Research Design and Methods, Sage Publications, 1994

  9. Empirical Approaches: Comparison

  10. Outline • Background on empirical studies • Empiricism in the end-user SE context • Problems for empiricism • Conclusion

  11. Three Aspects of Empiricism • Studies of EUSE (and SE) have two focal points • The ability of end users to use devices/processes • The devices and processes themselves • Evaluation and design of devices and processes are intertwined: • Summative evaluation helps us assess them • Formative evaluation helps us design them • We need families of empirical studies: • To generalize results • Studies inform and motivate further studies

  12. Building Empirical Knowledge through Families of Studies Generalization Summative Case Studies Controlled Experiments Controlled Experiments Hypothesis Testing environment, device user Exploratory, Theory Dev. Domain Analyses Think-Aloud, Formative Case Studies, Surveys

  13. Building Empirical Knowledge through Families of Studies Generalization Summative Case Studies Controlled Experiments Controlled Experiments Hypothesis Testing environment, device user Exploratory, Theory Dev. Domain Analyses Think-Aloud, Formative Case Studies, Surveys

  14. Empirical Studies in WEUSE Papers • Surveys • Scaffidi et al.: usage of abstraction, programming practices • Miller et al.: how users generate names for form fields • Segal: needs/characteristics of professional end user developers • Sutcliffe: costs/benefits perceived by users of a web-based content mgmt. system • Domain analysis • Elbaum et al.: fault types in Matlab programs • Controlled experiments • Fisher et al.: infrastructure support for spreadsheet studies

  15. Example: What You See is What You Test (WYSIWYT) Testing also flows upstream, marking other affected cells too. Cell turns more blue (more “tested”). At any time, user can check off correct value.

  16. Building Empirical Knowledge of End User SE through Families of Studies Generalization Summative Case Studies Controlled Experiments Controlled Experiments Hypothesis Testing environment, device user Exploratory, Theory Dev. Domain Analyses Think-Aloud, Formative Case Studies, Surveys

  17. Study 1: Effectiveness of DU-adequate test suites (TOSEM 1/01) • RQ: Can DU-adequate test suites detect faults more effectively than other types of test suites? • Compared DU-adequate vs randomly generated suites of the same size, for ability to detect various seeded faults, across 8 spreadsheets • Result: DU-adequate suites were significantly better than random at detecting faults

  18. Building Empirical Knowledge of End User SE through Families of Studies Generalization Summative Case Studies Controlled Experiments Controlled Experiments Hypothesis Testing environment, device user Exploratory, Theory Dev. Domain Analyses Think-Aloud, Formative Case Studies, Surveys

  19. Study 2: Usefulness of WYSIWYT (ICSE 6/00) • RQs: Are WYSIWYT users more (effective, efficient) than Ad-Hoc? • Compared two groups of users, one using WYSIWYT, one not, each on two spreadsheet validation tasks • Participants drawn from Undergraduate Computer Science classes • Participants using WYSIWYT were significantly better at creating DU-adequate suites, with less redundancy in testing

  20. Building Empirical Knowledge of End User SE through Families of Studies Generalization Summative Case Studies Controlled Experiments Controlled Experiments Hypothesis Testing environment, device user Exploratory, Theory Dev. Domain Analyses Think-Aloud, Formative Case Studies, Surveys

  21. Study 3: Usefulness of WYSIWYT with End Users (ICSM 11/01) • RQs: Are WYSIWYT users more (accurate, active at testing) than Ad-Hoc? • Compared two groups of users, one using WYSIWYT, one not, each on two spreadsheet modification tasks • Participants drawn from Undergraduate Business classes • Participants using WYSIWYT were more accurate in making modifications, and did more testing

  22. User can enter assertions Study 4: Using Assertions (ICSE 5/03) User can enter assertions System can figure out more assertions

  23. Building Empirical Knowledge of End User SE through Families of Studies Generalization Summative Case Studies Controlled Experiments Controlled Experiments Hypothesis Testing environment, device user Exploratory, Theory Dev. Domain Analyses Think-Aloud, Formative Case Studies, Surveys

  24. Study 4: Using Assertions (ICSE 5/03) • RQs: will end users use assertions and do they understand the devices • Observed persons as they worked with Forms/3 spreadsheets with assertion facilities provided

  25. Study 4: Using Assertions (ICSE 5/03) There’s got to be something wrong with the formula!

  26. Outline • Background on empirical studies • Empiricism in the end-user SE context • Problems for empiricism in end-user SE • Conclusion

  27. Problems for Empiricism in EUSE • Threats to validity – factors that limit our ability to draw valid conclusions • External: ability to generalize • Internal: ability to correctly infer connections between dependent and independent variables • Construct: ability of dependent variable to capture the effect being measured • Conclusion: ability to apply statistical tests

  28. External Validity • Subjects (participants) aren’t representative • Programs (objects) aren’t representative • Environments aren’t representative • Problems are trivial or atypical

  29. Internal Validity • Learning effects, expectation bias, … • Non-homogeneity among groups (different in experience, training, motivation) • Devices or measurement tools faulty • Timings are affected by external events • The act of observing can change behavior (of users, certainly, but also of artifacts)

  30. Construct Validity • Lines of code may not adequately represent amount of work done • Test coverage may not be a valid surrogate for fault detection ability • Successful generation of values doesn’t guarantee successful use of values • Self-grading may not provide an accurate measure of confidence

  31. Conclusion Validity • Small sample sizes • Populations don’t meet requirements for use of statistical tests • Data distributions don’t meet requirements for use of statistical tests

  32. Other Problems • Cost of experimentation • Difficulty of finding suitable subjects • Difficulty of finding suitable objects • Difficulty of getting the design right

  33. Outline • Background on empirical studies • Empiricism in the end-user SE context • Problems for empiricism in end-user SE • Conclusion

  34. Questions Addressed • How can we use empirical studies to better understand issues/approaches in end user SE? • Via families of appropriate studies, using feedback and replication • What are some of the problems empiricists working on end-user SE face? • Threats to validity, many particular to this area • Costs, and issues for experiment design/setup • What are some of the opportunities for software engineering researchers working in this area? • Myriad, given the range of study types applicable • Better still with collaboration

  35. Empirically Assessing End User Software Engineering Techniques Gregg Rothermel Department of Computer Science and Engineering University of Nebraska -- Lincoln

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