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posted by martyb on Monday June 26 2017, @11:08PM   Printer-friendly
from the He's-checking-his-list,-he's-checking-it-twice... dept.

Unreal Engine continues to develop as new code is added and previously written code is changed. What is the inevitable consequence of ongoing development in a project? The emergence of new bugs in the code that a programmer wants to identify as early as possible. One of the ways to reduce the number of errors is the use of a static analyzer like PVS-Studio. Moreover, the analyzer is not only evolving, but also constantly learning to look for new error patterns, some of which we will discuss in this article. If you care about code quality, this article is for you.

[I debated running this story as it was specific to Unreal Engine and PVS-Studio. Stepping back and looking at the larger picture of static code analysis, there seems to be plenty of room for discussion. What, if any, static code analyzers have you used? How helpful were they? Was it effective in finding [obscure] bugs? How much time did running the analysis consume? Was it an automated part of your build process? How many false positives did you run into compared to actual bugs. On an entirely different perspective, is it easier to find coding errors in compiled code or interpreted? --martyb]


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  • (Score: 2) by DutchUncle on Tuesday June 27 2017, @03:30PM

    by DutchUncle (5370) on Tuesday June 27 2017, @03:30PM (#531947)

    While the story may be "specific to Unreal Engine and PVS-Studio", the concept is not, and posting this as a "business case study" might help convince the managers who still can't understand why they should pay for such a product (yes, we still have some).

    This development team is using static analysis for games; I can tell you that there are life-safety products running right now that haven't been checked as thoroughly.

    We did get people to try CodeSonar, and aside from being very slow, it generated an overwhelming ration of false positives. The effort to winnow the wheat from the chaff was a PR disaster, overcoming the value of a handful of very useful finds. Then the IT department managed to mess up the licensing for a few months, and the experiment was considered a failure.

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