Balisage Paper: Case study: Quality assurance and quality control techniques in an XML data conversion project

Count function points in source to estimate effort

The basic metric we use to estimate the size of a conversion project or subproject is to count the conversion function points in the input data, defined as follows:

The function point count allows us to estimate the programming effort. In our experience, as a rule-of-thumb starting point, specification, programming and QC come out to about an hour per function point. Already-specified function points can usually be deducted from estimate.

Obviously, other metrics could be used. In determining how much context should count, experience suggested that no context (i.e. all bold considered the same) seemed too little, multiple levels of context (i.e. article/para/bold and section/para/bold considered different) seemed too much, and one level of context seemed about the right balance. By right balance we mean that the resulting function point count varied approximately with the programming effort required.

Benefit: This metric is objective, transparent, and repeatable.

Count function points in target to estimate slope

When available, an estimation of the function points in the target allows us to estimate the slope, or difficulty, of the conversion.

Example:

Note that conversion effort is sometimes more closely related to the number of output markup combinations that must be produced than to the number of input markup combinations. For example, it's much easier to convert both foreign and pub-title to italic than it is to map italic to either foreign or pub-title depending on other clues. So, with one-to-one (slope = 1) as the baseline, greater bulk-up factors typically represent greater complexity, difficulty and effort in the conversion.

But usually, by the time output is available for analysis, the conversion is done. As target markup is often not available, slope is more commonly estimated by the number of text-pattern-to-element rows in the specification. Or, a smaller sample is used to estimate slope, and then this slope is assumed to apply to the fuller data set.

Recommendation: during conversion keep slope modest. Slopes much higher than level can be defined to be data enhancements, rather than data conversions. As such, they may be more successfully undertaken once the basic conversion is complete. This is especially true in contexts such as the present case study, where the initial conversion collapsed hundreds of sometimes contradictory SGML DTDs to a single consistent XML DTD.

Autogenerate tight schemas to discover variation

Using free tools such as inst2xsd, any number of XML files can be used to create a schema. inst2xsd

A schema can be generated for an initial set of files, and then this schema can be used to validate additional files. The resulting validation errors indicate new input variations that must be accomodated.

This same technique can equally well be applied to conversion outputs. New patterns in the outputs can indicate where downstream processes may need to be extended, or may be indications of a conversion process that's gone off the rails.

Benefit: Auto-generated tight schemas are one way to highlight variation in the data.

List parent-child pairs to guide specification

Creating a simple list of all parent/child pairs in the input can be used to create a simple framework for specification.

Input	Context	Output	Notes
@align	colspec	to-do	to-do
@align	entry	to-do	to-do
b	entry	to-do	to-do
b	paragraph	to-do	to-do

These parent/child pairs are the same that we used as the definition of conversion function point for estimation purposes above, and so we already have generated these lists.

This same list can provide a simple starting skeleton for the conversion script:

<template match="colspec/@align | entry/@align">
 <call-template name="to-do"/>
</template>
<template match="entry/b | paragraph/b">
 <call-template name="to-do"/>
</template>

Always program for context

Mapping without context is deceptively fast:

<template match="b">
 <call-template name="map-to-bold"/>
</template>

But new contexts often require new consideration:

<template match="entry/b">
 <call-template name="map-to-heading-cell"/>
</template>
<template match="paragraph/b">
 <call-template name="map-to-bold"/>
</template>

Benefit: Defensive programming avoids accidents before they happen.

Always program for all content

We may script a paragraph by writing code for each of the following attributes:

But if we do that, we will lose:

...unless we script for and all other attributes.

Benefit: Avoid silent loss of data.

Compare source to target for lost or duplicated content

The goal in comparing conversion input to conversion output is to note where we've lost or duplicated content. The basic concept is easy: delete all the markup and compare the content that remains. The execution is rather more difficult:

Nonetheless, we acheived good results by preprocessing both input and output files, rescuing some content from markup and duplicated and deleting content as required. Then we sort runs of text (another problem: determine what constitutes a run of text) in order to overcome issues with purposely reordered content. Finally, we use a side-by-side comparison tool to highlight mismatches between the massaged source and target.

Benefit: Catch lost and duplicated content.

Autogenerate of word-wheels to highlight anomalous data

I was first introduced to the concept of a word wheel in the context of Folio Views™ in the early 1990s. Folio Folio Views was (and is) software for searching and browsing large sets of textual information - at the time, typically delivered on removable media. Folio Views for searching of predefined fields (in XML terms, typically metadata elements or semantic inline elements) across the data. One special feature, when searching fields, was autocompletion. When the users cursor was placed in the the search-form box corresponding to a particular field, all the values for that field were shown in an sorted list of unique values - called the word wheel. Just as with modern autocompletion, this list was updated as characters were typed in the search box.

The word wheel was a wonderful searching enhancement when it was introduced. But it could be a terrible embarrassment to the publisher if the source data was not clean. If searching for provinces, it was a wonderful help to be provided with a short list starting with Alberta and British Columbia. But if Alberta were mispelled anywhere in the thousands of documents being searched, it would show up in the short autocompletion list: Alberta, Alberto, British Columbia. Worse, if there were numerical values in a text field, they would sort to the top, making themselves all the more obvious.

The idea of adapting this word wheel behavior to become a quality control tool is simple. For each text-containing element in the converted output, with the exception of free-text fields like paragraphs, uniquely sort each of the observed values. Then ask a human to review the resulting sorted lists, looking for anomalies.

This approach very quickly highlights anomalous data, making it jump out to the human reviewer. At the same time, it avoids the wasted effort of reviewing thousands of redundantly correct values.

In the discussion that followed, Steve DeRose suggested that, in addition to an alphabetized list, a ranked list would give an additional insight. A ranked list would have the most common values at the top: not very interesting. And at the bottom, it would have the one-off values: possibly errors but also possibly one-off oddities. In the middle, you would have a sweet spot where you might find repeated errors.

Benefit: Makes human QC efficient.

Use an XQuery-capable XML database to quickly review data

When a suspicion arises, perhaps as a result of an odd word wheel entry or an unexplained mismatch between input and output content, it becomes very important that the offending data can be found quickly. And not only the specific example should be found, but we should review other places where similar behavior is likely to have occured. Having all input and all output instantly searchable using XQuery in invaluable. Given the availability of free and easy tools such as BaseX, there's really no excuse not to equip every team member with easy access to all the data. BaseX