Write at least two random and systematic errors in Computer Engineering

Question

Write at least two most important random errors in Computer engineering.

Write at least two most important systematic errors in Computer engineering.

Answer 1

I do not know what counts as random, and what counts as systematic,

but here are some famous and common bugs:

Famous insufficient representation:

- Y2K: Only two digits used to represent a year.

- Arian 5: Only 16 bits used to represent acceleration.

Famous timing:

- Space shuttle: The first flight was scrapped because one clock drifted off.

- Patriot missile: A clock drifted, causing Patriot missile to miss income Scud missile

Common security bugs:

- SQL injection: Failure to check validity of user input.

- Buffer overrun: Failure to check string length before storing.

Common C programming bugs:

- "if (a = b)" instead of "if (a == b)"

- "if (a == b&c)" instead of "if (a == (b&c))"

- "for (unsigned i = 10; i >= 0; --i)"

Common parallel programming bugs:

- Failure to put operation into a critical region

- Failure to release a lock when waiting

Answer 2

Random Error Example and Causes

If you take multiple measurements, the values cluster around the true value. Thus, random error primarily affects precision. Typically, random error affects the last significant digit of a measurement.

The main reasons for random error are limitations of instruments, environmental factors, and slight variations in procedure. For example:

1. When weighing yourself on a scale, you position yourself slightly differently each time.
2. When taking a volume reading in a flask, you may read the value from a different angle each time.
3. Measuring the mass of a sample on an analytical balance may produce different values as air currents affect the balance or as water enters and leaves the specimen.
4. Measuring your height is affected by minor posture changes.
5. Measuring wind velocity depends on the height and time at which a measurement is taken. Multiple readings must be taken and averaged because gusts and changes in direction affect the value.
6. Readings must be estimated when they fall between marks on a scale or when the thickness of a measurement marking is taken into account.

Because random error always occurs and cannot be predicted, it's important to take multiple data points and average them to get a sense of the amount of variation and estimate the true value.

Systematic Error Example and Causes

Systematic error is predictable and either constant or else proportional to the measurement. Systematic errors primarily influence a measurement's accuracy.

Typical causes of systematic error include observational error, imperfect instrument calibration, and environmental interference. For example:

1. Forgetting to tare or zero a balance produces mass measurements that are always "off" by the same amount. An error caused by not setting an instrument to zero prior to its use is called an offset error.
2. Not reading the meniscus at eye level for a volume measurement will always result in an inaccurate reading. The value will be consistently low or high, depending on whether the reading is taken from above or below the mark.
3. Measuring length with a metal ruler will give a different result at a cold temperature than at a hot temperature, due to thermal expansion of the material.
4. An improperly calibrated thermometer may give accurate readings within a certain temperature range, but become inaccurate at higher or lower temperatures.
5. Measured distance is different using a new cloth measuring tape versus an older, stretched one. Proportional errors of this type are called scale factor errors.
6. Drift occurs when successive readings become consistently lower or higher over time. Electronic equipment tends to be susceptible to drift. Many other instruments are affected by (usually positive) drift, as the device warms up.

Once its cause is identified, systematic error may be reduced to an extent. Systematic error can be minimized by routinely calibrating equipment, using controls in experiments, warming up instruments prior to taking readings, and comparing values against standards.

While random errors can be minimized by increasing sample size and averaging data, it's harder to compensate for systematic error. The best way to avoid systematic error is to be familiar with the limitations of instruments and experienced with their correct use.