RESEARCH EXPERT

Paul VarcholikJoshua Thompson

EEL 6883 – Software Engineering IISpring 2009

Background Academic Research

Literature Reviews Conferences Journals

Material collected from the Internet Google Scholar

How do researchers organize the papers they find?

Hard copies On-Disk Directory Structures

Background (cont.) Needs

Storage and quick retrieval of research papers Collaboration with colleagues User-provided reviews Annotated references

Existing Tools 2collab.com Mendeley Zotero Papers (Mac-only) Wikipedia comparison

High-Level ArchitectureDatabase

Data Laye

rUI

5 Assemblies 1 Common 1 Data Layer 1 Unit Test 2 UI

1 Web 1 Windows Forms (WinForms)

First Iteration Requirements gathering, initial design,

and implementation Web-based system Foundation set, key features available Large scope required feature pull-back UI lacking polish

Second Iteration Windows Forms (WinForms) UI Same base code – database and data

layer with some extensions Attempts at auto-extraction of meta-data

Iteration Metrics Comparison

First Iteration Second Iteration

180 files ~4,500 ELOC 57 classes and

enumerations 15 database tables 88 stored procedures 87 unit tests

Files ~9,650 ELOC 92 classes and

enumerations 16 database tables 100 stored procedures 96 unit tests

UI Comparison

Web Windows

Unit Testing

Discussion (cont.) Low complexity

Cyclomatic complexity Risk1-10 A simple, low risk program

11-20 A more complex program, moderate risk

21-50 A complex, high risk program

Greater than 50 An un-testable program (very high risk)

Assembly Cyclomatic complexity Unit Test 0.73

Data Layer 1.10

Common 1.54

Windows Client 1.16

Average 1.13

Discussion (cont.) High maintainability

Assembly MaintainabilityUnit Test 90.75

Data Layer 80.69

Common 82.64

Windows Client 64.34

Average 67.98 *

You can think of the score as a percentage grade, numbers closer to 100 are better.

* The formula for average complexity is logarithmic (the numbers don’t add up like sums)

PDF Parsing Metadata

Issue HeadingTitleAuthorsAbstractKeywords

PDF Parsing (cont.) Using PDFBox libraries for PDF reading

and manipulation Three methods for parsing PDFs

AutomaticXML basedUser-driven image based

PDF Parsing (cont.) Automatic parsing

Uses heuristics to determine metadata○ Font sizes○ Relative positioning○ Specific tokens

Pros○ No user input required○ Can provide reasonable guesses

Cons○ Makes assumptions○ Does not always work 100%○ Difficulties with text grabbing

PDF Parsing (cont.)

PDF Parsing (cont.) XML Parsing

Paper formats are specified○ Order of metadata○ Relative font sizes○ Token delimiters

Pros○ More effective than automatic parsing○ No direct user input required

Cons○ Requires manual input for each publication

source

PDF Parsing (cont.) User-Driven Image Based Parsing

Display Page 1User draws rectangles around metadataUses automatic parsing as an initial guess

○ User can review/modify the resultsPros

○ Uses automatic and user-driven methodsCons

○ Requires user input

PDF Parsing

Demonstration

Discussion Interesting uses of .NET Reflection Object Registry Difficulties of PDF Parsing

Approaches to resolving these difficulties○ Publication source templates○ User input○ Cut-and-paste

Future Work Integrated meta-data parsing Group-User-Repository access roles Author ranking Advanced searching Annotated references Additional document types (e.g. MS Word) More UI polish

Server selectionReview attachment improvementsAdministration features

Questions?

Research ExpertPaul VarcholikJoshua Thompson

EEL 6883 – Software Engineering IISpring 2009