V+Ü Empirical Evaluation in Informatics (Empirische Bewertung in der Informatik) SS 2018

This is the homepage of the lecture Empirical Evaluation in Informatics (Vorlesung "Empirische Bewertung in der Informatik") and its corresponding tutorial (Übung).

Description

As an engineering discipline, Informatics is constantly developing new artifacts such as methods, languages/notations or concrete software systems. In most cases, the functional efficiency and effectiveness of these solutions for the intended purpose is not obvious -- especially not in comparison to other already existing solutions for the same or similar purpose.

For this reason, methods for evaluating the efficacy of these solutions must be a routine part of Informatics. Evaluation is needed by those who create new solutions (that is, in research and development), but also by the users, as these need to evaluate the expected efficacy specifically for their situation. These evaluations need to be empirical (that is, based on observation), because the problems are nearly always too complicated for an analytical (that is, a purely thought-based) approach.

This lecture presents the most important empirical evaluation methods and explains where these have been used (using examples) and should be used, how to use them and what to consider when doing so.

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Administration

Lecturers

• Lutz Prechelt (lecture)
• Franz Zieris (tutorial)

Requirements/target group, classification, credit points etc.

see entry in the KVV course syllabus

Registration

Dates

• The lecture is held every Monday from 10:15 to 11:45 in room 046, Takustr. 9
  • The first lecture is on Monday, 2018-04-16
• The tutorial takes place every Monday from 12:15 to 13:45 in room 046, Takustr. 9
  • The first tutorial is on Monday, 2018-04-16
• Written exam: Monday, 2018-07-23, 09:59, Takustr. 9, room 046
• Post-exam review (Klausureinsicht): Wednesday, 2018-10-17, 15:59 until at least 16:30, room 055, Takustr. 9

Language

• The course language is German, but the actual slides and practice sheets are in English.
• The exam will be formulated in German, but answers may be given in English, too.

Examination modalities

Necessary criteria for obtaining the credit points:

• Completion of at least 80% of the tasks on the practice sheets
• active participation in the tutorial
• passing of the written examination (dictionary may be used)

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Content

Lecture topics

The lecture divides into three sections:

• Introduction (3 weeks): Introduces the basic ideas of empiricism and discusses quality characteristics for empirical studies (lectures 1 to 3).
• Methods (7 weeks): Presents basic aspects of and approaches to various empirical methods and illustrates them with concrete examples from the scientific literature.
• Data analysis (2 weeks): Empirical studies always generate raw data first which may partly be of qualitative and partly of quantitative nature. The research results only arise from the data's analysis and interpretation. The topic of the analysis of quantitative data is so comprehensive that you may dedicate an entire degree to it (statistics).
  This section gives the first introduction to the analysis of quantitative data. (The completely different analysis of qualitative data is beyond the scope of this lecture.)

1. (16.4.2018) Introduction - The role of empiricism: (Video 2018-04)
   • Term "empirical evaluation"; theory, construction, empiricism; status of empiricism in Informatics
   • Hypothetical examples of use
   • quality criteria: credibility, relevance
   • Note: scale types
2. (23.04.2018) The scientific method: (Video 2018-04)
   • Science and methods for gaining insights; classification of Informatics
   • The scientific method; variables, hypotheses, control; internal and external validity; validity, reliability, relevance
3. (30.04.2018) How to lie with statistics: (Video 2018-04)
   • When looking at somebody else's conclusions from data: What is actually meant? What specifically? How can they know it? What is not said?
   • Does the measurement distort the meaning? Is the sample biased?, etc.
   • Material: book on the topic; Study on alternative ink; article with arguments against hypothesis testing: "The earth is round (p < 0.05)".

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4. (07.05.2018) Empirical approach: (Video 2018-05)
   • steps: formulate aim and question; select method and design study; create study situation; collect data; evaluate findings; interpret results.
   • example: N-version programming (article, reply to the criticisms against it)
5. (14.05.2018) Survey: (Video 2018-05)
   • example: relevance of different topics in Informatics education (article)
   • method: selection of aims; selection of group to be interviewed; design and validation of the questionnaire; execution of the survey; evaluation; interpretation
6. (28.05.2018) Controlled experiment: (Video 2014-05)
   • example 1: flow charts versus pseudo-code (article, criticized prior work)
   • method: control and constancy; problems with reaching constancy; techniques for reaching constancy
   • example 2: use of design pattern documentation (article)
7. (04.06.2018) Quasi experiment: (Video 2018-06)
   • example 1: comparison of 7 programming languages (article, detailed technical report)
   • method: like controlled experiment, but with incomplete control (mostly: no randomization)
   • example 2: influence of work place conditions on productivity (article)
8. (11.06.2018) Benchmarking: (Video 2018-06)
   • example 1: SPEC CPU2000 (article)
   • Benchmark = measurement + task + comparison; problems (costs, task selection, overfitting); quality characteristics (accessibility, effort, clarity, portability, scalability, relevance) (article)
   • example 2: TREC (article)

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9. (18.06.2018) Data analysis - basic terminology: (Video 2018-06)
   • steps: make data available; validate data; explore data; analyze data (measure, model or compare). Some visualization techniques.
   • example data set (TPC tpmC): raw data (original format), raw data (prepared manually), Import procedure with recoding
10. (25.06.2018) Data analysis - techniques: (Video 2018-06)
    • Samples and populations; average value; variability; comparison of samples: significance test, confidence interval; bootstrap; relations between variables: plots, linear models, correlations, local models (loess)
    • Article: "A tour through the visualization zoo"

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11. (02.07.2018) Case study: (Video 2018-07)
    • example 1: Familiarization with a software team (article)
    • method: characteristics of case studies; what is the 'case'?; use of many data types; triangulation; validity dimensions
    • example 2: An unconventional methods for für requirements inspections (article)
12. (09.07.2018) Other methods: (Video 2018-07)
    • The method landscape; simulation; software archeology (studies on the basis of existing data); literature study;
    • example simulation: scaling of P2P file sharing (article)
    • example software archeology: code decline (article)
    • example literature study: a model of the effectiveness of reviews (article)

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13. (16.07.2018) Summary and advice: (Video 2018-07)
    • Role of empiricism; quality criteria; generic method; advantages and disadvantages of the methods; practical advice (for data analysis; for conclusion-drawing; for final presentation); outlook

Aims of the tutorials

• Tutorial 1 to 3 (concerning R)
  • To get to know the possibilities of a free, comprehensive and modern statistics software and gain basic skills with it.
  • To get to know a new way of thinking for programming ("programming with data") and practice it.
  • Realize how enlightening a data analysis may be in some cases and how useless in others.
• Tutorial 4 to 13 (project: empirical study)
  • To have gone through the design process of an empirical study oneself and to realize how many aspects must be considered.
  • To experience how many good ideas you may have and how many others possibly are still missing.
  • To realize how important it is to work accurately (because a correction of mistakes is often impossible and usually causes a huge amount of extra work).
  • To have had the gee-whiz experience of analyzing data which nobody else in this world has seen so far.

Practice sheets

(These links will be added continuously as the course proceeds.)

• Preparation for the tutorial: install R
  • version 3.4.4, see r-project.org.

• Practice sheet 1: get to know the data evaluation software "R",
  • R_intro_session.r, for Task 1-3
  • Screencast of that session (2007)
• Practice sheet 2: practice R, part 1
  • data.zip (contains junit.tsv, junit20.tsv, zile.tsv, jikes.tsv)
• Practice sheet 3: practice R, part 2
• Practice sheet 4: practice R, part 3; Survey: topic
• Practice sheet 5: Survey: method and first version of questionnaire
• Practice sheet 6: Survey: revise questionnaire
• Practice sheet 7: Survey: polish questionnaire
• Practice sheet 8: Survey: pilot test, recruit participants
• Practice sheet 9: Survey: interim report, Evaluating research: Experiments
• Practice sheet 10: Survey: validate data, prepare analysis
• Practice sheet 11: Evaluating research: Quantitative Data Analysis
• Practice sheet 12: Survey: data analysis, final presentation
• Practice sheet 13: Survey: final report
  • LaTeX template

Changes over the years

(minor changes are made every year)

• 2004: Lecture first held.
• 2005: Lecture: only minor changes. Tutorial: broader choice of topics for the surveys.
• 2010: Lecture and tutorial both held in English.

Literature

• Set CRAN = http://cran.mirroring.de

• see slides and links in the content list
• R-Dokumentation, especially An Introduction to R and Statistical Computing - Einführung in R
• Bad Science An English language blog written by Dr. Ben Goldacre, which criticises poorly-constructed science. Browsing through it will provide you with many good examples of how not to conduct empirical studies. Contains links to many other similar websites.

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