Towards More Natural Functional Programming Languages

1. Towards MoreNatural Functional Programming Languages Brad A. Myers Human-Computer Interaction Institute School of Computer Science Carnegie Mellon University http://www.cs.cmu.edu/~bam bam@cs.cmu.edu

2. The User Interface of Programming Languages • Programming is a human activity • Want to improve the ability of people to program • It makes sense to look at the human side

3. “Millions for compilers but hardly a penny for understanding human programming language use. Now, programming languages are obviously symmetrical, the computer on one side, the programmer on the other. In an appropriate science of computer languages, one would expect that half the effort would be on the computer side, understanding how to translate the languages into executable form, and half on the human side, understanding how to design languages that are easy or productive to use.... The human and computer parts of programming languages have developed in radical asymmetry.” • Allen Newell and Stuart Card, 1985

4. What is “Usability”? • Usability = “The effectiveness, efficiency, and satisfaction with which users can achieve tasks in a particular environment of a product.” • Components: • Learnability: Easy to learn so users can get started rapidly. • Effectiveness: Experts can use it effectively and with high productivity. • Low Error rate: Users make few errors. • Satisfaction: Pleasant to use. No frustrations for users. • Similar to motivations for functional languages

5. Why Human Computer Interaction? • The field of Human Computer Interaction studies how to improve and evaluate usability • Data, knowledge that can guide designs • To make systems more usable • Techniques for evaluating usability • So claims can be substantiated • So improvements can be made

6. Not just professional programmers anymore • By 2005, 55 million end-user programmers • Compared to only 2.75 million professional programmers Who are the Programmers?

7. Design of New Languages • How make design decisions? • Based on math, logic, type theory • Designer’s intuition or sense of aesthetics • Similarity to other languages • But many have known problems • Key concept: • If you care about usability: Can leverage off of what is known andwhat can be learned about peopleto guide design decisions

8. What we are doing... Studying the People

9. Buy a paint that is not red or blue Examples of Problems The men and women here raise your hands! if ((isMan x) && (isWoman x)) then (raise_hands x) else () • (This issue with “and” applies to other natural languages as well.) ( ) ( ) if (( not isRed || isBlue ) x) then buy x else () • Research shows that these differences between natural languages and computer languages hurt understanding

10. My Research Goals • Make programming significantly easier to learn and more effective for non-professional programmers and beginners • Try to provide a more objective basis for usability decisions for programming language design • Apply results of Empirical Studies of Programmers, Psychology of Programming, and Human-Computer Interaction to programming language design • New studies • Design new programming languages and environments based on these results

11. Multiple Criteria • Focusing on learnability and naturalness for beginners • Less emphasis: • Scalability • Provability • Efficiency • Mathematical or Logic properties • Similarity to other familiar languages • etc.

12. Gentle Slope Systems DifficultyofUse Program Complexity and Sophistication

13. Gentle Slope Systems Programming in C++ MFC DifficultyofUse Program Complexity and Sophistication

14. Gentle Slope Systems Programming in C++ FunctionalLanguages? MFC UI libraries DifficultyofUse Program Complexity and Sophistication

15. Gentle Slope Systems Visual Basic Programming in C++ FunctionalLanguages? C++ Programming MFC UI libraries DifficultyofUse Basic Program Complexity and Sophistication

16. Gentle Slope Systems Visual Basic Programming in C++ FunctionalLanguages? C Programming MFC UI libraries DifficultyofUse Basic My Goal Program Complexity and Sophistication

17. What is “Natural Programming”? • Attempt to make programming closer to the way people think • Make programming “more natural” • First, have to find out how people think about algorithms, data structures, etc. • Note: Not “natural language” • Still creating a formal language

18. Why Might Being Natural be Good? • “Programming is the process of transforming a mental plan into one that is compatible with the computer.” — Jean-Michel Hoc • So process might be easier if transformation is smaller • Closeness of mapping • "The closer the programming world is to the problem world, the easier the problem-solving ought to be.… Conventional textual languages are a long way from that goal." — Green and Petre

19. Why Might Being Natural be Good? • Example: • Inserting item into 3rd place of high score list • Conventional Languages: • Loop, starting at end of array, shuffle items down, then insert

20. Example: Why Might Being Natural be Good? • Directness (as in “Direct Manipulation”) • “Distance between one's goals and the actions required by the system to achieve those goals.”— Hutchins, Hollan and Norman vs. VB: Let Shape1.FillColor = &H00FF00FF& ML: SetColor ( Shape1, 0x00FF00FF )

21. Background Research • Empirical Studies of Programmers, Psychology of Programming, and HCI results not being used in the design of new languages • 30 years of research on what makes languages hard to learn and error-prone • Java / C# looping, etc. • Summarized in our comprehensive tech report • John Pane and Brad Myers, “Usability Issues in the Design of Novice Programming Systems” TR# CMU-CS-96-132. Aug, 1996.http://www.cs.cmu.edu/~pane/cmu-cs-96-132.html

22. Examples of Problems Identified • Promote Locality and Avoid Hidden Dependencies • Type definitions often are far from the use • Code readability is of key importance • Don’t try to reduce keystrokes if makes less readable • Inheritance and object-oriented design are very difficult • Beware of misleading appearances • When novices and experts mis-read code • Avoid subtle distinctions in syntax • E.g., a=b vs. a==b; () vs. [] vs. {}; >= vs. => vs. ->

23. More Examples • People expect consistency with external representations and usage (math, English, etc.): • People will search for an analogue in their experience that is similar to the syntax • “and”; a=a+1; a=2 vs. 2=a; 1<a<10; • ML: ~ is unary negative, - for subtraction: 5 - ~2 • So, if different meaning, should have different presentation • Significant difficulties in finding bugs due to invisible data, dependencies, and control flow

24. HCI Methods for Analyzing Languages • Analyze languages as user interfaces • Green’s Cognitive Dimensions — Green and Petre, 1996, “Usability Analysis of VP Environments: A ‘Cognitive Dimensions Framework’. Journal of VL&C, 7(2): 131-174 • 13 dimensions • Nielsen’s heuristic analysis principles — Nielsen, J., Usability Engineering. 1993, Boston: Academic Press • 10 principles • Can also perform usability studies for specific issues

25. Consistency • Both a Cognitive Dimension and a Heuristic Analysis principle • C++ uses the word "static" to mean at least 3 different things • In C++, can use int a,b; to define globals or locals, but not as procedure parameters • Should be able to copy code and use the same code elsewhere • In Visual Basic, to assign something you use “=”unless is an object, in which case you use “Set” and “=” "foo = 15" vs. "Set foo = object“ • ML: “fun f x = 0” vs. “case e of x => 0”

26. Error-Proneness • HCI Principle = Prevent errors • C and C++ array bounds errors • Requiring the "break" in each branch of C, C++ switch statements causes many errors (still in Java, fixed in C#) • Small typos can result in compilable programs that perform incorrectly, e.g., "=" for "==” or"x-=3” vs. "x=-3“ orfun f(SOME _)=... (a constructor pattern) vs.fun f(SOME_)=... (a variable)

27. Good Error Messages • Should be: clear, helpful, precise, constructive • Not “syntax error” • In C++, so much flexibility, compiler often doesn’t know where error is • Similar problems with type inference systems SML/NJ: stdIn:30.1-30.4 Error: operator and operand don't agree [tycon mismatch] operator domain: ?.t operand: ?.t in expression: f B

28. Closeness of Mapping • HCI principle = Speak the User's Language • Expressions of algorithms close to the way users think of them • Also, syntactic Issues: • C++ uses "void" to mean "none", "char" to mean 8-bit number, ... • Visual Basic uses "Dim" to declare variables and "wend" to end while loops • Arrays start at 0 whereas people think of counting from 1 • Case sensitivity

29. Viscosity • Resistance to local change • To change parameters of a function in C++, have to edit .h file and .cpp file, plus all call sites • Changing an “if” statement into a “do” statement was difficult in early structure editors • VLs are very difficult due to layout issues • May have to reposition all lines and boxes to make room and neaten resulting drawing • May need to disconnect and reconnect many wires • Need for correct indenting may make Haskell programs resistant to editing • But good editor can help

30. Less is More • HCI principle (“keep it simple”) • C, C++ have 16 levels of precedence that have to be memorized, some of which are left-associative and some are right-associative. Consider: a=b+=c=+d*e+++f==gwhich is a legal statement in C++ and C • Deep nesting in functional languages • “Too many parentheses”

31. Help the user get started with the system • Small things should be simple • Programs that do small things must stilloften be very large, e.g., creating awindow containing a single red rectangle • The 2-pages needed in Motif to do “Hello World” • “zero” lines in Visual Basic • In Java, it still requires: class HelloWorldApp { public static void main(String[] args) { System.out.println("Hello World!"); } } • Note 3 kinds of parentheses, 9 special words • ML: print"Hello World!"

32. Other Issues • Many more, see:http://www.cs.cmu.edu/~NatProg/langeval.html • You can send me examples from each other’s systems! • But these are mainly good for analysis • Given a design question, how answer it?

33. Our Research • Lots of gaps in prior research on people and programming • Develop knowledge that can be used in design • Ph.D. thesis of John Pane • Available at: http://www.cs.cmu.edu/~pane/thesis/ • Evaluate: • How people express algorithms and think about tasks • Vocabulary and notations used • Related to the HCI principles of “know the user”, “task analysis”, and “closeness of mapping”

34. Our Studies so far • How people naturally express programming concepts and algorithms 1) Nine scenes from PacMan 2) Transforming and calculating data in a spreadsheet • Specific issues of language design 3) Selecting specific objects from a group (“and”, “or”, “not”)

35. Experimental Design • Question should not bias the answer • So use pictures instead of textual descriptions • Concentrate on kids, non-programmers • Subjects should not be “tainted” by existing programming languages • Tested that the results generalize to adults and programmers

36. Study 1 Usually Pacman moves like this. Now let's say we add a wall. Pacman moves like this. Not like this. Do this: Write a statement that summarizes how I (as the computer) should move Pacman in relation to the presence or absence of other things.

37. Second Study • Whether similar results from other domains and with adults • Developed 11 questions with scenarios using spreadsheets • To test database access and operations • More conventionally “computational”

38. Example Question, 2nd Study Question 4 • Describe in detailed steps what the computer should do tocategorize these people into 2 groups of ‘Gold’ and ‘Black’. Firstname Lastname Firstname Lastname No. Group No. Group 1 Sandra Bullock 1 Sandra Bullock Gold 2 Bill Clinton 2 Bill Clinton Gold 3 Cindy Crawford 3 Cindy Crawford Gold 4 Tom Cruise 4 Tom Cruise Gold 5 Bill Gates 5 Bill Gates Black 6 Whitney Houston 6 Whitney Houston Gold 7 Michael Jordan 7 Michael Jordan Gold 8 Jay Leno 8 Jay Leno Black 9 David Letterman 9 David Letterman Black 10 Will Smith 10 Will Smith Gold

39. Results • Rule-based style “If PacMan loses all his lives, its game over.” • Some use of Constraint style: “Pacman cannot go through a wall.” • Aggregate operations instead of iterations“The monsters turn blue and run away” “Subtract 20,000 from all elements in Round 2” • — These tend to eliminate control structures

40. More Results • The words “AND” and “THEN” often used for sequencing instead of as a logical operator “The monsters turn color and start to back up.” • Boolean expression (AND, OR) not common • Usually had mutually exclusive rules“If I press the up arrow, PacMan goes up. If I press the down arrow, PacMan goes down, …” • General case first, then exceptions“When you encounter a ghost, it should kill you. But if you get a big pill first you can eat them.”

41. Yet More Results • Most arithmetic used natural language style“When PacMan eats a big dot, the score goes up 100.” • Operations suggest data as lists, not arrays • People don’t make space before inserting • Objects normally moving “If PacMan hits a wall, he stops.” • so objects remember their own state • 2/3 of the first study subjects drew pictures • Usually to define the initial state

42. Third Study: Select Objects from a Group • Concentrate on a known problematic area • Use of AND, OR, NOT • Often eliminated from Web searching • Newsweek reports that less than 6% of users manage to use “and”, “or”, “+”, “-” • Still dominant in all programming languages • First: generate queries given results • Then, answer queries • Form-based and Textual formats • Order was counter-balanced

43. Generate Queries

44. Answer Queries

45. Results • Using “unless” did not help accuracy“select the objects that are blue unless the objects are square”vs.“select the objects that match blue and not square • “And” was a Boolean conjunction sometimes“select the objects that match blue and circle”vs.“select the objects that match blue and the objects that match circle” • Precedence of “not” varied“select the objects that match not red and square”64% interpreted as “(not red) and square”“select the objects that match not triangle or green”67% interpreted as “not (triangle or green)”

46. More results • 2-D forms helped for generation • 94% correct with match forms, vs.85% correct with text (p<.0001) (blue and not square) or (circle and not green)

47. Implications for New Languages • For increased usability for novices: • Use event-based style for dynamic events • Work to minimize the need for control structures and variables • Provide operations on groups of objects • Data structures that combine the capabilities oflists + arrays + sets • Support simple arithmetic in natural language style (“add 1 to score”) • Avoid the use of the word “and” altogether

48. New Language and System: HANDS Human-centered Advances for Novices to Develop Software • Video

49. Properties of HANDS • Goal: Allow children age 10 to createinteractive games and simulations • Programming in the small (in the tiny) • Event based computation model • Metaphor of agent manipulating cards • All data is visible as properties of the cards • All operations work on singletons or lists • No distinction in syntax • Can generate lists on the fly with queries • Minimize need for control structures • Minimize need for local variables

50. More Properties of Hands • Verbose Language • Easier to read • No precedence • Does use parentheses • But just one kind! • Environment provide lots of help with syntax and graphics • Tries to be extremely consistent, and also apply other HCI rules • For example, combines IF, CASE (switch), andCOND (from Lisp) into one construct