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The Empirical Investigation of Perspective-Based Reading: Data analysis

Questions about the best way to perform code reviews go back almost to the start of software development. The perspective-based reading approach focuses reviewers’ attention on the needs of the users of the document/code, e.g., tester, user, designer, etc, and “The Empirical Investigation of Perspective-Based Reading” is probably the most widely cited paper on the subject. This paper is so widely cited I decided it was worth taking the time to email the authors of a 20 year old paper asking if the original data was available and could I have a copy to use in a book I am working on. Filippo Lanubile’s reply included two files containing the data (original files, converted files+code)!

How do you compare the performance of different approaches to finding problems in documents/code? Start with experienced subjects, to minimize learning effects during the experiment (doing this also makes any interesting results an easier sell; professional developers know how unrealistic student performance tends to be); the performance of subjects using what they know has to be measured first, learning another technique first would contaminate any subsequent performance measurements.

In this study subjects reviewed four documents over two days; the documents were two NASA specifications and two generic domain specifications (bank ATM and parking garage); the documents were seeded with faults. Subjects were split into two groups and read documents in the following sequences:

                Group 1     Group 2
Day 1
                NASA A      NASA B
                ATM         PG
Day 2
                Perspective-based reading training
                PG          ATM
                NASA B      NASA A

The data contains repeated measurements of the same subject (i.e., their performance on different documents using one of two techniques), so mixed-model regression has to be used to build a model.

I built two models, one for number of faults detected and the another for the number of false positive faults flagged (i.e., something that was not a fault flagged as a fault).

The two significant predictors of percentage of known faults detected were kind of document (higher percentage detected in the NASA documents) and order of document processing on each day (higher percentage reported on the first document; switching document kind ordering across groups would have enabled more detail to be teased out).

The false positive model was more complicated, predictors included number of pages reviewed (i.e., more pages reviewed more false positive reports; no surprise here), perspective-based reading technique used (this also included an interaction with number of years of experience) and kind of document.

So use of perspective-based reading did not make a noticeable difference (the false positive impact was in amongst other factors). Possible reasons that come to mind include subjects not being given enough time to switch reading techniques (people need time to change established habits) and some of the other reading techniques used may have been better/worse than perspective-based reading and overall averaged out to no difference.

This paper is worth reading for the discussion of the issues involved in trying to control factors that may have a noticeable impact on experimental results and the practical issues of using professional developers as subjects (the authors clearly put a lot of effort into doing things right).

Please let me know if you build any interesting model using the data.

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