The metrics are computed and compared to pre-established guidelines and historical data. The results of these comparisons are used to guide modifications to the code to improve the corresponding code qualities. Many modern static analysis tools generate reports that graphically present the analysis results and recommend potential resolutions to identified problems. No matter what you pick, your developers will find that code review is a great way to find bugs, mentor new hires, and share information.
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Metrics provide management with critical insight into how to support strategic decision making processes. When considered separately, a metric such as the number of defects per 1000 lines of code provides very little business meaning. However, a metric such as the number of security breaches per 1000 lines of code provides a much more useful and interesting relative value. It can be used to compare and contrast a given system’s security defect density against similarly sized systems and thus provide management with useful data for decision making. There are two distinct classes of quantitative software metrics, absolute and relative. Absolute metrics, such as the number of lines of code, do not involve uncertainty. There can be one and only one correct numerical representation of a given absolute metric.
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In contrast, relative metrics are a numeric representation of an attribute that cannot be directly measured, such as the degree of difficulty in testing for buffer overflows. There is no objective, absolute way to measure such an attribute. Multiple variables are factored into an estimation of the degree of testing difficulty, and any numeric representation is only an approximation. Metrics analysis looks at a quantitative measure of the degree to which the code under consideration possesses a given attribute. An attribute is a characteristic or a property of the code. The process of using code metrics begins by deriving the metrics that are appropriate for the code under consideration.
Imagine a developer in Hyderabad opening Outlook to discover 25 emails from different people discussing aspects of three different code changes he’s made over the last few days. It will take a while just to dig though that before any real work can begin. Before I list out the pros and cons, I’d like you to consider a certain effect that only this type of review exhibits. Because the author is controlling the pace of the review, often the reviewer doesn’t get a chance to do a good job. The reviewer might not be given enough time to ponder a complex portion of code.
The reviewing developer is deeply involved in the code, giving great thought to the issues and consequences arising from different implementations. On the one hand, this gives the reviewer lots of inspection time and a deep insight into the problem at hand, so perhaps this means the review is more effective. Some people suggest using both techniques – pair-programming for the deep review and a follow-up standard review for fresh eyes. Although this takes a lot of developer time to implement, it would seem that this technique would find the greatest number of defects. It’s also hard to manage multiple reviews at the same time.
Just make sure you implement a technique that doesn’t aggravate them so much that they revolt. There’s a controversial issue about whether pair-programming reviews are better, worse, or complementary to more standard reviews.
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The reviewer doesn’t get a chance to poke around other source files to check for side-effects or verify that API’s are being used correctly. The most obvious advantage of over-the-shoulder reviews is simplicity in execution. It can also be deployed whenever you need it most – an especially complicated change or an alteration to a "stable" code branch.
