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Topes: Enabling End-User Topes: Enabling End-User Programmers to Validate and Reformat Programmers to Validate and Reformat
DataData
Christopher Scaffidi
Committee:
Mary Shaw (chair) Institute for Software Research, Carnegie Mellon University
Sebastian Elbaum Computer Science & Engineering, University of Nebraska-Lincoln
Jim Herbsleb Institute for Software Research, Carnegie Mellon University
Brad Myers Human-Computer Interaction Institute, Carnegie Mellon University
22
Target populationTarget population
• In 2012, there will be 90 million computer end users in American workplaces.
• Of these, at least 55 million will create spreadsheets, databases, web applications, or other programs.– Spreadsheets for computing budgets– Spreadsheets and databases for storing information– Web applications for collecting data from coworkers
And similar programs for automating a wide range of tedious or error-prone work tasks.
Introduction Requirements Topes Tools Evaluation Conclusion
33
Contextual inquiry:Contextual inquiry:What are the problems of end users?What are the problems of end users?
Observed 3 administrative assistants, 4 managers, and 3 webmasters/graphic designers (1-3 hrs, each)
Introduction Requirements Topes Tools Evaluation Conclusion
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Lots of manual labor—Lots of manual labor—validating and reformatting stringsvalidating and reformatting strings
• Building a staff roster, merging data from web sites:– Had to scrutinize data to identify questionable values
(e.g.: CMU campus phone numbers are usually 268-xxxx but 269-xxxx might be right)
– Had to manually transform data to consistent format(e.g.: Put person names in Lastname, Firstname format)
Introduction Requirements Topes Tools Evaluation Conclusion
55
Another person’s task: validate web forms--Another person’s task: validate web forms--but he didn’t know JavaScript / regexpsbut he didn’t know JavaScript / regexps
Introduction Requirements Topes Tools Evaluation Conclusion
66
Collaborations of programmers withCollaborations of programmers withwidely varying skills, interests, concernswidely varying skills, interests, concerns
• Interviewing creators of Hurricane Katrina “person locator” sites (helping survivors publish their status)
• 4 managers in IT firms, 1 student, 1 graphic designer
– 2 people each created a site on their own– 4 people collaborated with other programmers
(principally on site aggregation)
Introduction Requirements Topes Tools Evaluation Conclusion
77
Hurricane Katrina “Person Locator” site:Hurricane Katrina “Person Locator” site:Many inputs unvalidatedMany inputs unvalidated
Introduction Requirements Topes Tools Evaluation Conclusion
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Hurricane Katrina sites are not alone in Hurricane Katrina sites are not alone in lacking input validation.lacking input validation.
• Eg: Google Base web application–13 primary web forms –Even numeric fields accept unreasonable inputs (such as a salary of “-45”)
• If professional programmers can’t get this right, then it’s unsurprising that those 90 million end users also have so much trouble.
So many unvalidated inputs. So many data errors. So much time to find mistakes. So many millions of people laboriously reformatting data by hand.
We need a better way!
Introduction Requirements Topes Tools Evaluation Conclusion
99
OutlineOutline
1. Requirements for a better model
2. Topes• Model for describing data• Tools for creating/using topes
3. Evaluations
4. Conclusion
Introduction Requirements Topes Tools Evaluation Conclusion
1010
Underlying problem: abstraction mismatchUnderlying problem: abstraction mismatch
• Tools support strings, integers, floats, maybe dates.
• Problem domain involves higher-level data categories:
– Person names “Scaffidi, Chris”, “Chris Scaffidi”
– CMU phone numbers “8-1234”, “x8-1234”
– CMU room numbers “WeH 4623”, “Wean 4623”
Introduction Requirements Topes Tools Evaluation Conclusion
1111
Approach: Create a new abstraction for Approach: Create a new abstraction for each category of dataeach category of data
• Like software “libraries,” implementations of these abstractions could be reused in many programs.
• Abstractions would need to include functions for:– Recognizing instances of the category
(for automating data validation)
– Transforming instances among various formats
(for automating data reformatting)
Introduction Requirements Topes Tools Evaluation Conclusion
1212
1. Identify valid, invalid, and 1. Identify valid, invalid, and questionable valuesquestionable values
• Data is sometimes questionable… yet valid.– E.g.: an unusually long email address– In practice, person names and other proper nouns are never
validated with regexps… too brittle.– Life is full of corner cases and exceptions.
• If code can identify questionable data, then it can double-check the data:– Ask an application end user to confirm the input– Flag the input for checking by a system administrator– Compare the value to a list of known exceptions– Call up a server and see if it can confirm the value
Introduction Requirements Topes Tools Evaluation Conclusion
1313
2. Capture reformatting rules2. Capture reformatting rules
• Two different strings can be equivalent.– What if an end user types a date in the wrong format?– “Jan-3-2007” and “1/3/2007” mean the same thing because of
the category that they are in: date.– Sometimes the interpretation is ambiguous. In real life,
preferences and experience guide interpretation.
• If code can transform among formats, then it can put data in an unambiguous format as needed.– Display result so users can check/fix interpretation
Introduction Requirements Topes Tools Evaluation Conclusion
1414
3. User-extensibility3. User-extensibility
• Many kinds of data are organization-specific
• But users at those organizations know what the data values mean—take advantage of what they know…
• Users can describe the constrained parts of data.– Eg: CMU room numbers, “EDSH 303”, have a building name
and an internal room number– Valid data obeys intra- and inter-part constraints.
Introduction Requirements Topes Tools Evaluation Conclusion
1515
4. Reusability across programming 4. Reusability across programming environments (“platforms”)environments (“platforms”)
• Validity does not depend on whether the string is in a spreadsheet or a webform or a database
• To validate a kind of data, people don’t want to write– JavaScript for webforms on the client side– C#/Java/PHP for webforms on the server side– Stored procedures for databases– VBScript for spreadsheets
Introduction Requirements Topes Tools Evaluation Conclusion
1616
Limitations of existing approachesLimitations of existing approaches
Types do not support questionable values
Grammars (eg: regexps, CFGs, Lapis) do not either, and cannot reformat
Tools to integrate heterogeneous databases require a professional DBA and are specific to database systems (ie: not spreadsheets, webforms, etc).
Cues, Forms/3, -calculus, Slate, etc, infer numerical constraints but not constraints on strings, and they are tied to specific programming platforms
Information extraction algorithms rely on grammatical cues that are absent during validation
Introduction Requirements Topes Tools Evaluation Conclusion
1717
TopesTopes
• A “tope” = a platform-independent abstraction that describes how to recognize and reformat instances of a data category
• Greek word for “place,” because each corresponds to a data category with a natural place in the problem domain
Introduction Requirements Topes Tools Evaluation Conclusion
1818
A tope is a graph.A tope is a graph.Node = format, edge = transformationNode = format, edge = transformation
Notional representation for a CMU room number tope…
Formal building name& room number
Elliot Dunlap Smith Hall 225
Colloquial building name& room number
Smith 225
Building abbreviation& room number
EDSH 225
Introduction Requirements Topes Tools Evaluation Conclusion
1919
A tope has functions for recognizing and A tope has functions for recognizing and transforming instances of a data categorytransforming instances of a data category• Each tope implementation has executable functions:
– 1 isa:string[0,1] function per format, for recognizing instances of the format (a fuzzy set)
– 0 or more trf:stringstring functions linking formats, for transforming values from one format to another
• Validation function:(str) = max(isaf(str))where f ranges over tope’s formats– Valid when (str) = 1– Invalid when (str) = 0– Questionable when 0 < (str) < 1
Introduction Requirements Topes Tools Evaluation Conclusion
2020
Common kinds of topes:Common kinds of topes:enumerations and proper nouns enumerations and proper nouns
• Multi-format Enumerations, e.g: US states– “New York”, “CA”, maybe “Guam”
• Open-set proper nouns, e.g.: company names– Whitelist of definitely valid names (“Google”), with
alternate formats (e.g. “Google Corp”, “GOOG”)– Augmented with a pattern for promising inputs that
are not yet on the whitelist
Introduction Requirements Topes Tools Evaluation Conclusion
2121
Two other common kinds of topes:Two other common kinds of topes:numeric and hierarchicalnumeric and hierarchical
• Numeric, e.g.: human masses– Numeric and in a certain range– Values slightly outside range might be questionable– Sometimes labeled with an explicit unit– Transformation usually by multiplication
• Hierarchical, e.g.: address lines– Parts described with other topes (e.g.: “100 Main St.”
uses a numeric, a proper noun, and an enum)– Simple isas can be implemented with regexps.– Transformations involve permutation of parts, lookup
tables, and changes to separators & capitalization.
Introduction Requirements Topes Tools Evaluation Conclusion
2222
Role of good tool supportRole of good tool support
• Some simple isa functions could be implemented as– Enumerations– Regular expressions / formal grammars
• But for many topes, we also need to support questionable values and reformatting
• And usability can almost always be improved by tailoring the tools to the problem domain– Integrate with users’ familiar tools– Match the user interface to the problem’s structure
Introduction Requirements Topes Tools Evaluation Conclusion
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Topes in actionTopes in action
1. Users create data descriptions (abstract, user-friendly descriptions of data categories)
2. Users publish data descriptions on repositories.
3. Other users download data descriptions to cache.
4. System automatically generates tope implementations from data descriptions.
5. Tool add-ins help users browse their cache and associate topes with variables and input fields.
6. Add-ins get topes from local cache and call them at runtime to validate and reformat data.
Introduction Requirements Topes Tools Evaluation Conclusion
2424
What the user seesWhat the user sees
Introduction Requirements Topes Tools Evaluation Conclusion
User highlights cellsClicks “New” button on our Validation toolbar
2525
System infers a boilerplate topeSystem infers a boilerplate topeand presents it for review and customizationand presents it for review and customization
Induction steps:1. Identify number & word parts2. Align parts based on punctuation3. Infer simple constraints on parts
2525 Introduction Requirements Topes Tools Evaluation Conclusion
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User gives names to the partsUser gives names to the partsand edits constraintsand edits constraints
Features• Part names• Value whitelists• Testing features• Soft constraints (never / rarely / often / almost always / always)
Introduction Requirements Topes Tools Evaluation Conclusion
2727
System identifies typosSystem identifies typos
Introduction Requirements Topes Tools Evaluation Conclusion
Features• Targeted messages• Overridable• Filterable• Can add to “whitelist”• Integrated with Excel’s “reviewing” functionality
Checking inputs1. Convert description to CFG w/
constraints on productions2. Parse each input string3. For each constraint violation,
downgrade parse’s isa score
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Easy access to reformatting functionalityEasy access to reformatting functionality
Introduction Requirements Topes Tools Evaluation Conclusion
Reformatting string1. Parse with input format’s CFG2. For each part in target format,
a) Get node from parse treeb) Reformat node if needed (recurse)c) Concatenate (with separators if needed)
3. Validate result with target format’s CFG
2929
Recommending topes based on label and Recommending topes based on label and examples-to-matchexamples-to-match
Introduction Requirements Topes Tools Evaluation Conclusion
Efficient recommendation• Only consider a tope if its instances could possibly have the “character content” of each example string.(eg.: could this have 12 letters & 1 space?)
3030
Search repository by Search repository by label and/or exampleslabel and/or examples
Note: many repositories will be organization-specific
Introduction Requirements Topes Tools Evaluation Conclusion
3131
Integration with Visual Studio.NETIntegration with Visual Studio.NET
Introduction Requirements Topes Tools Evaluation Conclusion
Features• Targeted messages• Overridable• Drag & drop code generation
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Other integrations to date:Other integrations to date:CoScripter, Robofox, XML/HTML libraryCoScripter, Robofox, XML/HTML library
Introduction Requirements Topes Tools Evaluation Conclusion
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Other integration underwayOther integration underway
Introduction Requirements Topes Tools Evaluation Conclusion
• RedRover– Spreadsheet auditing– They already support formula auditing– Goal: Using topes for checking strings
• LogicBlox– Decision-support– Helping users enter data & make decisions from it– Goal: Using topes for validating data– Goal: Using topes for data de-duplication
3434
EvaluationEvaluation
• Many evaluations rely on the EUSES Spreadsheet Corpus (collected by Univ. Nebraska) – In particular, 4250 spreadsheet columns that
contained at least 20 strings
• These evaluations generally use the F1 statistic as a measure of accuracy1. Get strings from the corpus
2. Manually validate the strings
3. Automatically validate the strings (eg: with topes)
4. Compute F1 to check agreementF1 = precision * recall / ( (precision + recall)/2 )
Introduction Requirements Topes Tools Evaluation Conclusion
3535
Evaluating accuracyEvaluating accuracy
• Implemented topes for spreadsheet data– Created 32 topes for the most common categories
• Covering 1199 columns, which was ~69% of the 1713 categorized columns, or ~28% of all 4250 columns
• Up to 5 formats per tope
– Compared to current practice• Validate w/ tope, simulate asking user on questionable inputs, F1=0.7• Validate w/ regexps or enumerations if available, but accept all inputs
when no regexp or enumeration is available, F1=0.19
– Tope-based validation was 3 times as accurate• Big benefit from supporting multi-format topes• Moderate benefit from validating currently-unvalidated categories • Small benefit from double-checking questionable values
(~ 3% of inputs)
Introduction Requirements Topes Tools Evaluation Conclusion
3636
Evaluating reusabilityEvaluating reusability
• Reused spreadsheet-based topes on webform data– Downloaded data for 8 data categories on
Google Base and 5 in Hurricane Katrina website– Reused spreadsheet-based topes on the web data– Validation was even more accurate than on
spreadsheets
• F1=0.75 for Google Base, 0.92 for Hurricane Katrina• Website data had less formatting diversity than spreadsheets
Introduction Requirements Topes Tools Evaluation Conclusion
3737
Evaluating support for data cleaningEvaluating support for data cleaning
• Used topes to put web data into consistent formats– Again with the 5 columns in Hurricane Katrina website– Used transformation functions to put each string into
the most common format for that data category– Increased number of duplicate strings found by 10%
Introduction Requirements Topes Tools Evaluation Conclusion
3838
Evaluating usability for data validationEvaluating usability for data validation
• Users validating data with single-format topes– Between-subjects lab study– 8 users validated spreadsheet data with our tools;
for comparison, 8 users validated with Lapis patterns– Yes/no validation tasks (no questionable data)– Our tool users vs Lapis users
• Found three times as many typos (comparable F1 scores)• Were twice as fast• Reported significantly higher user satisfaction
– Our tool users vs users in earlier regexp study• Faster & more accurate
(Similar but not identical tasks: not statistically comparable)
Introduction Requirements Topes Tools Evaluation Conclusion
3939
Evaluating usability for data reformattingEvaluating usability for data reformatting
• Users reformatting data with multi-format topes– Within-subjects lab study– 9 users reformatted spreadsheet data by creating &
using topes; for comparison, they then did it manually– Effort of creating a tope “pays off” at only 47 strings
(further reuse is essentially “free”)– Every participant strongly preferred using our tools
instead of doing tasks manually
Introduction Requirements Topes Tools Evaluation Conclusion
4040
Evaluating tope recommendationsEvaluating tope recommendations
• Quickly recommend existing tope for data at hand– Supports keyword-based search + search-by-match
(eg: topes that match “888-555-1212”)– Evaluated by searching through topes for the 32 most
common data categories in EUSES spreadsheet corpus, using strings from corpus
– High accuracy: Recall over 80% (result set size = 5)– Adequate speed: User is likely to have a few dozen
topes on computer, taking under 1 sec to search
Introduction Requirements Topes Tools Evaluation Conclusion
4141
Closing the mismatch between Closing the mismatch between data abstractions and the real worlddata abstractions and the real world
• People often work with strings that are possibly-questionable instances of multi-format categories.
• These categories are application-agnostic and often common to many people.
• By capturing rules for validating and reformatting strings (including distinguishing questionable strings and multiple formats), topes…– Increase the accuracy of validation– Help users to accomplish validation and reformatting
activities quickly and effectively – Improve the reusability of validation code
Introduction Requirements Topes Tools Evaluation Conclusion
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Thank You…Thank You…
• To my committee and the entire EUSES Consortium for helpful suggestions
• To NSF for funding
Introduction Requirements Topes Tools Evaluation Conclusion
4343
ReferencesReferences
For more information on end users and topes- End users’ counts and needs: VL/HCC’05, VL/HCC’07- Topes model: ICSE’08- Format inferrence: ICEIS’07- Integration with other systems: WEUSE’08 & FSE’08- Our latest tools + usability validation: ISEUD’09 & IUI’09
For more information on some related work- Dependent types, eg: X. Ou, Dynamic Typing with Dependent Types, Tech Rpt TR-695-04, Princeton Univ, 2004
- Regexp induction, eg: K. Lerman, S. Minton. Learning the Common Structure of Data, Proc. AAAI, 2000.
- Lapis system: R. Miller, Lightweight structure in text, Tech Rpt CMU-CS-02-134, Carnegie Mellon Univ., 2002.
- SWYN regexp editor: A. Blackwell, See What You Need: Helping End-users to Build Abstractions, JVLC, 2001.
- Federated databases, eg: A. Sheth, J. Larsen, Federated database systems for managing distributed, heterogeneous, and autonomous databases, CSUR, 1990.
- ETL Tools, eg: E. Rahn, H. Do, Data Cleaning: Problems and Current Approaches, IEEE Data Eng. Bulletin, 2000.
- Potter’s Wheel: V. Raman, J. Hellerstein, Potter's Wheel: An Interactive Data Cleaning System, VLDB, 2001.
- Forms/3 : M. Burnett et al, End-user software engineering with assertions in the spreadsheet paradigm, ICSE, 2003.
- -calculus: M. Erwig, M. Burnett, Adding Apples and Oranges. Symp. Practical Aspects of Declarative Lang., 2002.
- Named entities, eg: Message Understanding Conference series.
Introduction Requirements Topes Tools Evaluation Conclusion
4444
Professional programmers use lots of tricks Professional programmers use lots of tricks to simplify validation code. Eg: njtransit.comto simplify validation code. Eg: njtransit.com
Split inputs into many easy-to-validate fields.Who cares if the user has to type tabs now,or if he can’t just copy-paste into one field?
Make users pick from drop-downs.Who cares if it’s faster for users to type
“NJ” or “1/2007”?(Disclaimer: drop-downs sometimes are good!)
I implemented this site in 2003.
Introduction Requirements Topes Tools Evaluation Conclusion
4545
Even with these tricks, writing validation is Even with these tricks, writing validation is still very time-consuming.still very time-consuming.
Overall, the site had over 1100 lines of JavaScript
just for validation….Plus equivalent server-side Java code (too bad code
isn’t platform-independent)
if (!rfcCheckEmail(frm.primaryemail.value)) return messageHelper(frm.primaryemail, "Please enter a valid Primary Email address.");var atloc = frm.primaryemail.value.indexOf('@');if (atloc > 31 || atloc < frm.primaryemail.value.length-33) return messageHelper(frm.primaryemail, "Sorry. You may only enter 32 characters or less for your email name\r\n”+ ”and 32 characters or less for your email domain (including @).");
Introduction Requirements Topes Tools Evaluation Conclusion
4646
That was worst case.That was worst case.Best case: reusable regexps.Best case: reusable regexps.
• Many IDEs allow the programmer to enter oneregular expression for validating each input field.– Usually, this drastically reduces the amount of code,
since most validation ain’t fancy.– Yet programmers don’t validate most inputs.
Introduction Requirements Topes Tools Evaluation Conclusion
4747
Users’ spreadsheets are rife with Users’ spreadsheets are rife with formatting inconsistencies & other typosformatting inconsistencies & other typos
In one study by Univ Nebraska, nearly 40% of spreadsheet cell values were strings (not numbers or dates).
Part of an actual spreadsheet on Carnegie Mellon’s public web site
Introduction Requirements Topes Tools Evaluation Conclusion
4848
Evaluating expressivenessEvaluating expressiveness
• Implemented topes for common webform inputs– Instrumented web browsers of 4 administrative
assistants for 3 weeks– Logged strings that they typed into forms – in a
regexp-masked format e.g.: [email protected] [a-z]{4}[0-9]@[A-Z]{3}.[A-Z]{3}
– Also logged strings nearby to textfields– Semi-automatically grouped strings into categories
e.g.: project number, expense type, email address, zip code
– Implemented 14 most common topes– Found 22 probable typos in user inputs (0.5%)
Introduction Requirements Topes Tools Evaluation Conclusion
4949
Tope Development Environment (TDE)Tope Development Environment (TDE)
Introduction Requirements Topes Tools Evaluation Conclusion
User’s finished data description
Strings from applications
Stringsfrom
applications
Boilerplate data description
Error messages and reformatted strings
Tope implementation and data description
Error messages andreformatted strings
Data description (download)
Data description (upload)
End user applications Microsoft Excel – spreadsheets Visual Studio.NET – web forms Robofox – web macros Vegemite/Co-Scripter – web macros
...
Topei
Toped++ Topeg
Add-ins
Remote repositories
Local repository
5050
As a tool builder, what do I have to do so As a tool builder, what do I have to do so that people can use topes in my tool?that people can use topes in my tool?
You need to make an add-in1. Figure out what kind of fields you want to help your
users validate/reformat(eg: spreadsheets’ cells; webforms’ textboxes)
2. Download our open source C# or Java API (library)
3. In your tool’s UI, add buttons and other widgets so user can select a tope for the fields; in your event handler, call our API methods
4. At runtime, pass field’s value (a string) to our API methods to validate or reformat strings
5. Display validation error messages; update value in UI
Introduction Requirements Topes Tools Evaluation Conclusion
5151
Recognizing multiple formats and Recognizing multiple formats and questionable inputs raises accuracyquestionable inputs raises accuracy
Condition 4: Hypothetical user has to help on ~ 3% of inputs
Condition 1: Recall = 0 (fails to identify any invalid inputs)
Introduction Requirements Topes Tools Evaluation Conclusion
5252
User’s finished data description
Strings from applications
Stringsfrom
applications
Boilerplate data description
Error messages and reformatted strings
Tope implementation and data description
Error messages and
reformatted strings
Data description (download)
Data description (upload)
End user applications
Microsoft Excel – spreadsheets
Visual Studio.NET – web forms
Robofox – web macros
Vegemite/Co-Scripter – web macros
...
Topei
Toped++ Topeg
Add-ins
Remote repositories
Local repository
5353
Imagine a world where…Imagine a world where…
• Code can ask an oracle, “Is this a person name?”, and the oracle replies yes, no, almost definitely, probably not, and other shades of gray.
• Code allows input in any reasonable format, since the code can ask the oracle to put the input into the format that is actually needed.
• Regardless of whether they are working in spreadsheets, webforms, or other programming environment, end users can teach the oracle about a new data category by concisely stating its parts and constraints.
Introduction Requirements Topes Tools Evaluation Conclusion
5454
Data errors reduce the usefulness of data.Data errors reduce the usefulness of data.
Even little typos impede data de-duplication.
Age is not useful for flying my helicopter to come rescue you.
Nor is a “city name” with 1 letter.
Introduction Requirements Topes Tools Evaluation Conclusion
5555
5656
3
14
55
90
0 10 20 30 40 50 60 70 80 90 100
professionalprogrammers
people who say they"do programming"
spreadsheet /database users
computer users
Projected population size (millions)
5757
5858
5959
6060
A Word-like part that almost always contains 1-6 words that each always have 1-8 lowercase letters per word and only hyphens or ampersands between words:
#PART : #WORDLIST : COUNT(#WORD)>=1 && COUNT(#WORD)<=6 {90}#WORDLIST : #WORD | #WORD #SEP #WORDLIST#WORD : #CHLIST : COUNT(#CH)>=1 && COUNT(#CH)<=8 {100}#CHLIST : #CH | #CH #CHLIST #CH : a|b|c|d|e|f|g|h|i|j|k|l|m|n|o|p|q|r|s|t|u|v|w|x|y|z#SEP : - | &
6161
6262
6363
6464
6565
6666
6767
6868