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In a world where about 95 percent of ATM transactions use COBOL, and at a time when a range of products remain built on decades-old code, AI headlines distract from the real software reliability issues at play. (Image: © Image Credit: Geralt / Pixabay) Laying the foundation for a successful AI deployment AI, like any traditional business system or application, needs a robust foundation. Before being able to come to grips with creating complex applications, engineers need to address immediate software reliability issues, first. Nearly all software ships with undiagnosed bugs that may turn into serious production incidents, resulting in client churn and burning hours of engineering resource later down the line.
It would be foolish to ignore the promise – and the threats – heralded by AI; it is equally foolish to ignore the problems and risks from the unreliability of today’s “regular” software. Commercial pressures mean that software development managers and their teams have to make trade-offs between code quality and the pressure to ship product features. The Economist writes that some of the neatest software ever written – by NASA’s Software Assurance Technology Centre – carried 0.1 errors per 1,000 lines of source code. Most software has a reliability of many orders of magnitude lower than that.
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Tricentis, a testing platform vendor, highlighted this problem in their January 2018 Software Fail Watch report. It analysed 606 software failures and found that over 3.6 billion people had been affected by these software problems, resulting in $1.7 trillion in lost revenue to software vendors. Database vendors are particularly vulnerable due to the highly competitive nature of the market, the complexity of the systems, and the high costs of unreliability.
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As a result, bugs must be addressed as early as possible in testing. Unfortunately many are difficult to identify, as they only subtly affect the program so they do not seem to appear in the testing phase at all. Once in production, these bugs can lead to severe outages and software failures. Software reliability To ensure businesses steer away from the above scenario, engineering departments are well advised to consider their software reliability strategy and take preventive measures to diagnose serious software defects before they cause havoc on customer site. So what can software development teams do to make their software more reliable? The revolution in testing (Continuous Integration, Test Driven Development, Fuzz Testing, etc) means that today thousands of automated tests can be run. A typical software project of a given size will be running thousands of times more tests than an equivalent project ten or twenty years ago. For the industry, this is a big leap forward.
But all these tests are a nightmare to triage if even a tiny fraction fail, particularly if they fail intermittently. One possible solution to this trillion-dollar problem of software reliability is software flight recording. By recording a program execution as it fails, engineering teams obtain a reliable reproducible test case that gives them total visibility into all the factors that led up to (and caused) a crash or program misbehaviour. This approach is especially effective against intermittent test failures, which are by nature very difficult to reproduce – a common problem in software development. Software failures can then be captured, replayed in a reversible-debugger and diagnosed orders of magnitude faster than with traditional techniques.
A solution like recording and replaying program execution allows software engineering teams to observe exactly what their program did at any point in time and why. This helps to speed up time-to-resolution and minimise customer disruption.
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