Software bug explained

A software bug is a bug in computer software.

A computer program with many or serious bugs may be described as buggy.

The effects of a software bug range from minor (such as a misspelled word in the user interface) to severe (such as frequent crashing).

Software bugs have been linked to disasters. Software bugs in the Therac-25 radiation therapy machine were directly responsible for patient deaths in the 1980s. In 1996, the European Space Agency's US$1 billion prototype Ariane 5 rocket was destroyed less than a minute after launch due to a bug in the on-board guidance computer program.[1] In 1994, an RAF Chinook helicopter crashed, killing 29; was initially blamed on pilot error, but was later thought to have been caused by a software bug in the engine-control computer.[2] Buggy software caused the early 21st century British Post Office scandal.[3]

In 2002, a study commissioned by the US Department of Commerce's National Institute of Standards and Technology concluded that "software bugs, or errors, are so prevalent and so detrimental that they cost the US economy an estimated $59 billion annually, or about 0.6 percent of the gross domestic product".[4]

Since the 1950s, some computer systems have been designed to detect or auto-correct various software errors during operations.

Terminology

Mistake metamorphism (from Greek meta = "change", morph = "form") refers to the evolution of a defect in the final stage of software deployment. Transformation of a "mistake" committed by an analyst in the early stages of the software development lifecycle, which leads to a "defect" in the final stage of the cycle has been called 'mistake metamorphism'.[5]

Different stages of a mistake in the development cycle may be described as mistake,[6] anomaly,[6] fault,[6] failure,[6] error,[6] exception,[6] crash,[6] glitch,bug,[6] defect,incident,[6] or side effect.

Controversy

Sometimes the use of bug to describe the behavior of software is contentious due to perception. Some suggest that the term should be abandoned; replaced with defect or error.

Some contend that bug implies that the defect arose on its own and push to use defect instead since it more clearly connotates caused by a human.[7]

Some contend that bug may be used to coverup an intentional design decision. In 2011, after receiving scrutiny from US Senator Al Franken for recording and storing users' locations in unencrypted files,[8] Apple called the behavior a bug. However, Justin Brookman of the Center for Democracy and Technology directly challenged that portrayal, stating "I'm glad that they are fixing what they call bugs, but I take exception with their strong denial that they track users."[9]

Prevention

Preventing bugs as early as possible in the software development process is a target of investment and innovation.[10] [11]

Language support

Newer programming languages tend to be designed to prevent common bugs based on vulnerabilities of existing languages. Lessons learned from older languages such as BASIC and C are used to inform the design of later languages such as C# and Rust.

Languages may include features such as a static type system, restricted namespaces and modular programming. For example, for a typed, compiled language (like C):

float num = "3";

is syntactically correct, but fails type checking since the right side, a string, cannot be assigned to a float variable. Compilation fails forcing this defect to be fixed before development progress can resume. With an interpreted language, a failure would not occur until later at runtime.

Some languages exclude features that easily lead to bugs, at the expense of slower performance the principle being that it is usually better to write simpler, slower correct code than complicated, buggy code. For example, the Java does not support pointer arithmetic which is generally fast, but is considered dangerous; relatively easy to cause a major bug.

Some languages include features that add runtime overhead in order to prevent some bugs. For example, many languages include runtime bounds checking and a way to handle out-of-bounds conditions instead of crashing.

A compiled language allows for detecting some typos (such as a misspelled identifier) before runtime which is earlier in the software development process than for an interpreted language.

Techniques

Programming techniques such as programming style and defensive programming are intended to prevent typos.

For example, a bug may be caused by a relatively minor, typographical error (typo) in the code. For example, this code executes function only if is true.

if (condition) foo;

But this code always executes :

if (condition); foo;

A convention that tends to prevent this particular issue is to require braces for a block even if it has just one line.

if (condition)

Enforcement of conventions may be manual (i.e. via code review) or via automated tools.

Specification

Some contend that writing a program specification which states the behavior of a program, can prevent bugs.

Some contend that formal specifications are impractical for anything but the shortest programs, because of problems of combinatorial explosion and indeterminacy.

Software testing

One goal of software testing is to find bugs.

Measurements during testing can provide an estimate of the number of likely bugs remaining. This becomes more reliable the longer a product is tested and developed.

Agile practices

Agile software development may involve frequent software releases with relatively small changes. Defects are revealed by user feedback.

With test-driven development (TDD), unit tests are written while writing the production code, and the production code is not considered complete until all tests complete successfully.

Static analysis

Tools for static code analysis help developers by inspecting the program text beyond the compiler's capabilities to spot potential problems. Although in general the problem of finding all programming errors given a specification is not solvable (see halting problem), these tools exploit the fact that human programmers tend to make certain kinds of simple mistakes often when writing software.

Instrumentation

Tools to monitor the performance of the software as it is running, either specifically to find problems such as bottlenecks or to give assurance as to correct working, may be embedded in the code explicitly (perhaps as simple as a statement saying PRINT "I AM HERE"), or provided as tools. It is often a surprise to find where most of the time is taken by a piece of code, and this removal of assumptions might cause the code to be rewritten.

Open source

Open source development allows anyone to examine source code. A school of thought popularized by Eric S. Raymond as Linus's law says that popular open-source software has more chance of having few or no bugs than other software, because "given enough eyeballs, all bugs are shallow".[12] This assertion has been disputed, however: computer security specialist Elias Levy wrote that "it is easy to hide vulnerabilities in complex, little understood and undocumented source code," because, "even if people are reviewing the code, that doesn't mean they're qualified to do so."[13] An example of an open-source software bug was the 2008 OpenSSL vulnerability in Debian.

Debugging

See main article: Debugging.

Debugging can be a significant part of the software development lifecycle. Maurice Wilkes, an early computing pioneer, described his realization in the late 1940s that“a good part of the remainder of my life was going to be spent in finding errors in my own programs”.[14]

A program known as a debugger can help a programmer find faulty code by examining the inner workings of a program such as executing code line-by-line and viewing variable values.

As an alternative to using a debugger, code may be instrumented with logic to output debug information to trace program execution and view values. Output is typically to console, window, log file or a hardware output (i.e. LED).

Some contend that locating a bug is something of an art.

It is not uncommon for a bug in one section of a program to cause failures in a different section, thus making it difficult to track, in an apparently unrelated part of the system. For example, an error in a graphics rendering routine causing a file I/O routine to fail.

Sometimes, the most difficult part of debugging is finding the cause of the bug. Once found, correcting the problem is sometimes easy if not trivial.

Sometimes, a bug is not an isolated flaw, but represents an error of thinking or planning on the part of the programmers. Often, such a logic error requires a section of the program to be overhauled or rewritten.

Some contend that as a part of code review, stepping through the code and imagining or transcribing the execution process may often find errors without ever reproducing the bug as such.

Typically, the first step in locating a bug is to reproduce it reliably. If unable to reproduce the issue, a programmer cannot find the cause of the bug and therefore cannot fix it.

Some bugs are revealed by inputs that may be difficult for the programmer to re-create. One cause of the Therac-25 radiation machine deaths was a bug (specifically, a race condition) that occurred only when the machine operator very rapidly entered a treatment plan; it took days of practice to become able to do this, so the bug did not manifest in testing or when the manufacturer attempted to duplicate it. Other bugs may stop occurring whenever the setup is augmented to help find the bug, such as running the program with a debugger; these are called heisenbugs (humorously named after the Heisenberg uncertainty principle).

Since the 1990s, particularly following the Ariane 5 Flight 501 disaster, interest in automated aids to debugging rose, such as static code analysis by abstract interpretation.[15]

Often, bugs come about during coding, but faulty design documentation may cause a bug. In some cases, changes to the code may eliminate the problem even though the code then no longer matches the documentation.

In an embedded system, the software is often modified to work around a hardware bug since it's cheaper than modifying the hardware.

Management

Bugs are managed via activities like documenting, categorizing, assigning, reproducing, correcting and releasing the corrected code.

Tools are often used to track bugs and other issues with software. Typically, different toolsare used by the software development team to track their workload than by customer service to track user feedback.[16]

A tracked item is often called bug, defect, ticket, issue, feature, or for agile software development, story or epic. Items are often categorized by aspects such as severity, priority and version number.

In a process sometimes called triage, choices are made for each bug about whether and when to fix it based on information such as the bug's severity and priority and external factors such as development schedules. Triage generally does not include investigation into cause. Triage may occur regularly. Triage generally consists of reviewing new bugs since the previous triage and maybe all open bugs. Attendees may include project manager, development manager, test manager, build manager, and technical experts.[17] [18]

Severity

Severity is a measure of impact the bug has.[19] This impact may be data loss, financial, loss of goodwill and wasted effort. Severity levels are not standardized, but differ by context such as industry and tracking tool. For example, a crash in a video game has a different impact than a crash in a bank server. Severity levels might be crash or hang, no workaround (user cannot accomplish a task), has workaround (user can still accomplish the task), visual defect (a misspelling for example), or documentation error. Another example set of severities: critical, high, low, blocker, trivial.[20] The severity of a bug may be a separate category to its priority for fixing, or the two may be quantified and managed separately.

A bug severe enough to delay the release of the product is called a show stopper.[21] [22]

Priority

Priority describes the importance of resolving the bug in relation to other bugs. Priorities might be numerical, such as 1 through 5, or named, such as critical, high, low, and deferred. The values might be similar or identical to severity ratings, even though priority is a different aspect.

Priority may be a combination of the bug's severity with the level of effort to fix. A bug with low severity but easy to fix may get a higher priority than a bug with moderate severity that requires significantly more effort to fix.

Patch

Bugs of sufficiently high priority may warrant a special release which is sometimes called a patch.

Maintenance release

A software release that emphasizes bug fixes may be called a maintenance release to differentiate it from a release that emphasizes new features or other changes.

Known issue

It is common practice to release software with known, low-priority bugs or other issues. Possible reasons include but are not limited to:

Implications

The amount and type of damage a software bug may cause affects decision-making, processes and policy regarding software quality. In applications such as human spaceflight, aviation, nuclear power, health care, public transport or automotive safety, since software flaws have the potential to cause human injury or even death, such software will have far more scrutiny and quality control than, for example, an online shopping website. In applications such as banking, where software flaws have the potential to cause serious financial damage to a bank or its customers, quality control is also more important than, say, a photo editing application.

Other than the damage caused by bugs, some of their cost is due to the effort invested in fixing them. In 1978, Lientz et al. showed that the median of projects invest 17 percent of the development effort in bug fixing.[25] In 2020, research on GitHub repositories showed the median is 20%.[26]

Cost

In 1994, NASA's Goddard Space Flight Center managed to reduce their average number of errors from 4.5 per 1000 lines of code (SLOC) down to 1 per 1000 SLOC.[27]

Another study in 1990 reported that exceptionally good software development processes can achieve deployment failure rates as low as 0.1 per 1000 SLOC.[28] This figure is iterated in literature such as Code Complete by Steve McConnell,[29] and the NASA study on Flight Software Complexity.[30] Some projects even attained zero defects: the firmware in the IBM Wheelwriter typewriter which consists of 63,000 SLOC, and the Space Shuttle software with 500,000 SLOC.

Benchmark

To facilitate reproducible research on testing and debugging, researchers use curated benchmarks of bugs:

Types

Some notable types of bugs:

Design error

A bug can be caused by insufficient or incorrect design based on the specification. For example, given that the specification is to alphabetize a list of words, a design bug might occur if the design does not account for symbols; resulting in incorrect alphabetization of words with symbols.

Arithmetic

Numerical operations can result in unexpected output, slow processing, or crashing.[33] Such a bug can be from a lack of awareness of the qualities of the data storage such as a loss of precision due to rounding, numerically unstable algorithms, arithmetic overflow and underflow, or from lack of awareness of how calculations are handled by different software coding languages such as division by zero which in some languages may throw an exception, and in others may return a special value such as NaN or infinity.

Control flow

See also: Logic error.

A control flow bug, a.k.a. logic error, is characterized by code that does not fail with an error, but does not have the expected behavior, such as infinite looping, infinite recursion, incorrect comparison in a conditional such as using the wrong comparison operator, and the off-by-one error.

Interfacing

Concurrency

Resourcing

Syntax

See also: Syntax error.

Teamwork

In politics

"Bugs in the System" report

The Open Technology Institute, run by the group, New America,[38] released a report "Bugs in the System" in August 2016 stating that U.S. policymakers should make reforms to help researchers identify and address software bugs. The report "highlights the need for reform in the field of software vulnerability discovery and disclosure."[39] One of the report's authors said that Congress has not done enough to address cyber software vulnerability, even though Congress has passed a number of bills to combat the larger issue of cyber security.

Government researchers, companies, and cyber security experts are the people who typically discover software flaws. The report calls for reforming computer crime and copyright laws.

In popular culture

See also

External links

Notes and References

  1. ARIANE 5 Flight 501 Failure Report by the Inquiry Board. July 23, 1996. 33–1996. The European Space Agency. Ariane 501 Inquiry Board report.
  2. Simon Rogerson. The Chinook Helicopter Disaster. IMIS Journal. 12. 2. April 2002. dead. http://web.archive.org/web/19930915000000/http://www.ccsr.cse.dmu.ac.uk/resources/general/ethicol/Ecv12no2.html. September 15, 1993. May 27, 2024. Alt URL
  3. News: Post Office scandal ruined lives, inquiry hears . . BBC News . 14 February 2022 .
  4. Web site: Software bugs cost US economy dear . June 10, 2009 . September 24, 2012 . dead . https://web.archive.org/web/20090610052743/http://www.nist.gov/public_affairs/releases/n02-10.htm . June 10, 2009 .
  5. Testing Experience. March 2012. testingexperience. Testing experience : te : the magazine for professional testers. Germany. 42 . 1866-5705.
  6. Book: 610.12-1990: IEEE Standard Glossary of Software Engineering Terminology. IEEE. December 31, 1990. 978-0-7381-0391-4. 10.1109/IEEESTD.1990.101064.
  7. Software Engineering Institute. News at SEI September 1999. September 1, 1999. SEI Interactive. 2. 3. Carnegie Mellon University.
  8. Gregg Keizer. Apple faces questions from Congress about iPhone tracking. April 21, 2011. Computerworld.
  9. Gregg Keizer. Apple denies tracking iPhone users, but promises changes. Computerworld. April 27, 2011.
  10. Book: Automated Defect Prevention: Best Practices in Software Management. Dorota Huizinga. Adam Kolawa. 978-0-470-04212-0. September 2007. Wiley-IEEE Computer Society Press.
  11. Book: McDonald, Marc . Musson, Robert . Smith, Ross . The Practical Guide to Defect Prevention . 2007 . Microsoft Press . 480 . 978-0-7356-2253-1 . mdy-all . registration .
  12. http://www.catb.org/~esr/writings/cathedral-bazaar/cathedral-bazaar/ar01s04.html "Release Early, Release Often"
  13. http://www.securityfocus.com/news/19 "Wide Open Source"
  14. Web site: Maurice Wilkes Quotes. QuoteFancy. April 28, 2024.
  15. Web site: PolySpace Technologies history. christele.faure.pagesperso-orange.fr. August 1, 2019.
  16. Allen . Mitch . May–June 2002 . Bug Tracking Basics: A beginner's guide to reporting and tracking defects . Software Testing & Quality Engineering Magazine . 4 . 3 . 20–24 . December 19, 2017.
  17. Book: Managing The Testing Process . 2nd . Rex Black. 2002. 19 June 2021. Wiley India Pvt. Limited. 978-8126503131. 139.
  18. Book: Shipping Greatness - Practical Lessons on Building and Launching Outstanding Software, Learned on the Job at Google and Amazon. Chris Vander Mey. 2012. O'Reilly Media. 978-1449336608. 79–81.
  19. Soleimani Neysiani. Behzad. Babamir. Seyed Morteza. Aritsugi. Masayoshi. 2020-10-01. Efficient feature extraction model for validation performance improvement of duplicate bug report detection in software bug triage systems. Information and Software Technology. en. 126. 106344. 10.1016/j.infsof.2020.106344. 219733047.
  20. Web site: 5.3. Anatomy of a Bug. bugzilla.org. live. https://web.archive.org/web/20130523121753/http://www.bugzilla.org/docs/4.4/en/html/bug_page.html. May 23, 2013.
  21. Encyclopedia: Show stopper . Glossary: defense acquisition acronyms and terms . 1989 . . Fort Belvoir, Virginia. Jones . Wilbur D. Jr. . 4 . 123 . 2027/mdp.39015061290758?urlappend=%3Bseq=163 . en. Hathitrust.
  22. Book: Zachary, G. Pascal . Show-stopper!: the breakneck race to create Windows NT and the next generation at Microsoft . . 1994 . 0029356717 . New York . 158 . en . registration . archive.org.
  23. The Next Generation 1996 Lexicon A to Z: Slipstream Release. Next Generation. 15 . March 1996. 41.
  24. Web site: 'It's Not a Bug, It's a Feature.' Trite – or Just Right?. wired.com. Nicholas . Carr. 2018.
  25. Characteristics of Application Software Maintenance. Communications of the ACM . 1978 . 466–471. 21. 10.1145/359511.359522. B. P.. Lientz . E. B.. Swanson . G. E.. Tompkins. 6 . 14950091 . free.
  26. Amit . Idan . Feitelson . Dror G.. 2020 . The Corrective Commit Probability Code Quality Metric . cs.SE . 2007.10912.
  27. Software Engineering Laboratory Series. An Overview of the Software Engineering Laboratory. December 1994. SEL-94-005.
  28. Engineering software under statistical quality control . . University of Tennessee – Harlan D. Mills Collection . Cobb . Richard H. . 6 . 7 . Mills . Harlan D. . 10.1109/52.60601 . 1990 . 46 . 538311 . en . 1937-4194 . Harlan Mills.
  29. Book: McConnell, Steven C. . Code Complete . Microsoft Press . 1993 . 978-1556154843 . Redmond, Washington. 611 . en . archive.org . (Cobb and Mills 1990) . Steve McConnell . registration.
  30. Gerard Holzmann. Appendix D – Software Complexity. Final Report: NASA Study on Flight Software Complexity (Daniel L. Dvorak (Ed.)). March 5, 2009. NASA Office of Chief Engineer Technical Excellence Program.
  31. Le Goues. Claire. Holtschulte. Neal. Smith. Edward K.. Brun. Yuriy. Devanbu. Premkumar. Forrest. Stephanie. Weimer. Westley. The ManyBugs and IntroClass Benchmarks for Automated Repair of C Programs. IEEE Transactions on Software Engineering. 41. 12. 2015. 1236–1256. 0098-5589. 10.1109/TSE.2015.2454513. free.
  32. Book: Just. René. Proceedings of the 2014 International Symposium on Software Testing and Analysis – ISSTA 2014. 437–440. Jalali. Darioush. Ernst. Michael D.. 12796895. Defects4J: a database of existing faults to enable controlled testing studies for Java programs. 2014. 10.1145/2610384.2628055. 9781450326452. 10.1.1.646.3086.
  33. Anthony Di Franco. Hui Guo. Cindy Rubio-González. A comprehensive study of real-world numerical bug characteristics. 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). November 23, 2017. IEEE. 10.1109/ASE.2017.8115662.
  34. Book: Kimbler, K. . [{{google books |plainurl=y |id=q7BSGKJrWxsC|page=8}} Feature Interactions in Telecommunications and Software Systems V]. 1998. IOS Press. 978-90-5199-431-5. 8.
  35. Book: Syed, Mahbubur Rahman. [{{google books |plainurl=y |id=e3rAmuQSUXkC|page=398}} Multimedia Networking: Technology, Management and Applications: Technology, Management and Applications]. 2001. Idea Group Inc (IGI). 978-1-59140-005-9. 398.
  36. Book: Chwan-Hwa (John) . Wu. J. David. Irwin. [{{google books |plainurl=y |id=bInNBQAAQBAJ|p=500}} Introduction to Computer Networks and Cybersecurity]. 2016. CRC Press. 978-1-4665-7214-0. 500.
  37. RFC 1263: "TCP Extensions Considered Harmful" quote: "the time to distribute the new version of the protocol to all hosts can be quite long (forever in fact). ... If there is the slightest incompatibly between old and new versions, chaos can result."
  38. Web site: Bugs in the System. Wilson. Andi. Schulman. Ross. Open Policy Institute. August 22, 2016. Bankston. Kevin. Herr. Trey. live. https://web.archive.org/web/20160921012606/https://na-production.s3.amazonaws.com/documents/Bugs-in-the-System-Final.pdf. September 21, 2016.
  39. Web site: Cyber reforms needed to strengthen software bug discovery and disclosure: New America report – Homeland Preparedness News. Rozens. Tracy. August 12, 2016. en-US. August 23, 2016.
  40. Book: Ullman, Ellen . 2004 . The Bug . . 978-1-250-00249-5 .