Software Quality Metrics Overview . Kan discusses several metrics in each of three groups of software quality metrics: product quality, in- process quality, and maintenance quality. He also describes the key metrics used by several major software developers and discusses software metrics data collection.

Product metrics describe the. Process metrics can be used to improve software.

Examples include the effectiveness of defect. Project metrics describe the project. Examples include the number of software. Some metrics belong to multiple categories. For. example, the in- process quality metrics of a project are both process metrics. Software quality metrics are a subset of software metrics that focus on the. In general, software.

Nonetheless, the project parameters such as the. Software. quality metrics can be divided further into end- product quality metrics and. The essence of software quality engineering is to. Moreover, we should view quality from the. In this chapter we discuss several metrics. Install Kali Linux Without Grubs more.

In the last sections we also describe the key. As discussed in Chapter 1, the de facto definition of software quality.

The. metrics we discuss here cover both levels: Mean time to failure. Defect density. Customer problems.

What is Defect? A defect is an error or a bug, in the application which is created. A programmer while designing and building the software can make mistakes or error. Mil-std-882b 30 march 1984 superseding mil-std-882a 28 june 1977 military standard mil-std-882b system safety program requirements amsc number f3329 fsc saft. This is only one of the latest in a long, dismal history of IT projects gone awry Definitions. Software means software and related materials. Rightholder (owner of all rights, whether exclusive or otherwise to the Software) means. A software bug is an error, flaw, failure or fault in a computer program or system that causes it to produce an incorrect or unexpected result, or to behave in.

A guide to fault detection and diagnosis as a core component of Operations Management Automation. OSHA Technical Manual (OTM)

Customer satisfaction. Intrinsic product quality is usually measured by the number of. Offline Study Material Ccna Discovery V4 0.

The MTTF metric is most often used with safety- critical systems such as. For instance, the.

U. S. In civilian airliners, the. Littlewood and Strigini, 1. The defect density metric, in. The two metrics are correlated but are different enough to merit close.

Errors in software requirements and software design documents are more frequent than errors in the source code itself, according to Computer Finance Magazine. Defect Life Cycle Software Quality Control Software Defect / Bug: Definition, Explanation, Classification, Details: DEFINITION A Software Defect / Bug is a condition. Overcoming these challenges are not as simple as finding the right software, establishing the set of best practices and implementing a BPM system.

First, one measures the time between failures, the other. Second, although it is difficult to separate defects and. According to the IEEE/ American National. Standards Institute (ANSI) standard (9. An error is a human mistake that results in incorrect software. The resulting fault is an accidental condition that causes a unit of the.

A defect is an anomaly in a product. A failure occurs when a functional unit of a software- related system can. From these definitions, the difference between a fault and a defect is.

For practical purposes, there is no difference between the two terms. Simply put, when an error occurs during the development process, a fault or a. In operational mode, failures are caused by. Sometimes a fault. Therefore, defect and failure do not have a one- to- one. Third, the defects that cause higher failure rates are usually discovered and.

The probability of failure associated with a latent defect is. For general- purpose computer systems or.

MTTF metric is more difficult to implement and may not be. Fourth, gathering data about time between failures is very expensive. It. requires recording the occurrence time of each software failure. It is sometimes. quite difficult to record the time for all the failures observed during testing. To be useful, time between failures data also requires a high.

This is perhaps the reason the MTTF metric is not widely. Finally, the defect rate metric (or the volume of defects) has another appeal. The defect rate of a product. Accordingly, there are two. We discuss the two types of.

Chapter 8. 4. 1. 1 The Defect Density Metric Although seemingly straightforward, comparing the defect rates of software. In this section we try to articulate the major. To define a rate, we first have to operationalize the numerator and the. As discussed in Chapter 3, the general. OFE) during a specific time frame. We have just discussed the definitions of. Because failures are defects materialized, we can.

The denominator is the size of the software, usually. KLOC) or in the number of function points.

In our experience with operating. For application software, most defects are normally. Lines of Code The lines of code (LOC) metric is anything but simple. The major problem. In. the early days of Assembler programming, in which one physical line was the same. LOC definition was clear. With the availability of.

Differences. between physical lines and instruction statements (or logical lines of code) and. LOCs. Jones. (1. 98. Count only executable lines. Count executable lines plus data definitions. Count executable lines, data definitions, and comments.

Count executable lines, data definitions, comments, and job control. Count lines as physical lines on an input screen. Count lines as terminated by logical delimiters. To illustrate the variations in LOC count practices, let us look at a few. In Boehm's well- known book. Software Engineering Economics (1.

LOC counting method counts. In Software Engineering Metrics and Models by Conte et al. This specifically includes all lines containing program headers. Thus their method is to count physical lines including prologues and data. In Programming Productivity. Jones (1. 98. 6), the source instruction (or logical lines of code) method.

The method used by IBM Rochester is also to count source instructions. The resultant differences in program size between counting physical lines and. It is not even known. In some languages such as BASIC. PASCAL, and C, several instruction statements can be entered on one physical. On the other hand, instruction statements and data declarations might span.

Languages. that have a fixed column format such as FORTRAN may have the. According to Jones. In contrast. for COBOL the difference is about 2. There are strengths and weaknesses of physical LOC and logical LOC (Jones. In general, logical statements are a somewhat more rational choice for. When any data on size of program products and their quality are. LOC counting should be described.

At the minimum, in. LOC data is involved, the author should state. LOC counting method is based on physical LOC or logical LOC. Furthermore, as discussed in Chapter 3, some companies may use the straight. LOC count (whatever LOC counting method is used) as the denominator for. Assembler- equivalent LOC based on some conversion ratios) for the. Therefore, industrywide standards should include the conversion.

Assembler. So far, very little research on. The conversion ratios published by Jones (1. As more and more high- level languages. When straight LOC count data is used, size and defect rate comparisons across.

Extreme caution should be exercised when comparing. LOC, defects, and time frame are not identical. Indeed, we do not recommend such. We recommend comparison against one's own history for the sake. NOTEThe LOC discussions in this section are in the context of defect rate.

For productivity studies, the problems with using LOC are more. A basic problem is that the amount of LOC in a softare program is. The purpose of software is to. Efficient design provides the functionality with lower. LOCs. Therefore, using LOC data to measure.

In addition to the level of languages issue, LOC data do. The LOC results are so misleading in productivity studies that. Jones states . For detailed discussions of LOC. Jones's work (1. 98. When a software product is released to the market for the first time, and. LOC count method is specified, it is relatively easy to state its. For example, statements such as the.

One needs to. measure the quality of the entire product as well as the portion of the product. The latter is the measurement of true development quality—the. Although the defect rate for the entire. To calculate defect rate for the new and changed code. LOC count: The entire software product as well as the new and. Defect tracking: Defects must be tracked to the release. When calculating the defect.

These tasks are enabled by the practice of change flagging. Specifically. when a new function is added or an enhancement is made to an existing function.

ID) number through the use of comments. The ID is linked to the requirements.

This linkage procedure is part of the software configuration. If the change- flagging IDs and requirements IDs are further. LOC counting tools can use the. The change- flagging. When a defect is reported and the fault zone determined, the. The new and changed LOC counts can also be obtained via the delta- library.

By comparing program modules in the original library with the new.

Why Systems Fail – Ben. Meadowcroft. com.

Introduction: The aim of this report is to put forward the major causes of Systems failure, to analyse the proposed causes and to justify them with examples of actual examples taken from the recent past (Section. The report then proposes a classification scheme into which the various proposed and justified factors may be placed. This scheme provides a generalised view of the areas where systems failure may be caused (Section 3). The generalised view given by this classification scheme then allows us to conclude some general techniques and/or practices, which will greatly reduce the number of system failures and thus decrease the impact these failures have (Section 4). The system failures used as examples and proposed here are not just those that cause the complete collapse of a system but also those that through erroneous operation of that system make large impressions in other ways, e.

Analysis of Causes of System Failure: 2. Poor development practices.

As a cause of system failure, poor development practices are one of the most significant. This is due to the complex nature of modern software. An example of poor development practices causing a system failure can be found in the experience of the Pentagon’s National Reconnaissance Office (NRO). The inadequate testing of the delivery system of Titan IV rocket. Two Titan rockets were lost, meaning that expensive military equipment necessary to the U. S. Governments defence program (namely early warning satellites) were unable to be deployed. The head of the N.

R. O. See http: //spacer. Testing of systems already in operation is also important in being prepared for potential system failures. The most obvious example is with the case of the “YK bug”. There can be problems with testing operational systems however, as the example of the Loss Angeles emergency preparedness system shows.

Due to an error which occurred while testing around four million gallons of raw sewage was dumped into the Sepulveda Dam area. See http: //catless.

Risks/2. 0. 4. 6. Incorrect assumptions with regard to system requirements. Incorrect assumptions may be made on the part of the software developers or indeed may be made on the part of the entity requiring the software system. So what exactly can faulty assumptions cause to happen? There are many problems that can result from faulty assumption made by the development team. An example of this factor causing major problems can be found in the experience of Nuclear Regulatory Commission. A program, which was developed to test models of nuclear reactors called Shock II, miscalculated some important calculations needed to ensure the test models would survive a massive earthquake.

A summer student who wrote a module, which converted a vector into a magnitude by summing components, rather than summing absolute values, caused the miscalculation. This error of the earthquake testing system, discovered after the nuclear reactors had been built and were providing energy, meant that five nuclear power stations had to be closed down for checks and reinforcement. The analysis of potential problems within the plants and correcting those problems took months.

During that time the utility companies had to provide electricity by a different method, relying again on the more expensive oil and coal power stations. To provide enough power to make up for the shortfall produced by the closure of the nuclear plants the older style plants had too consume an estimated 1. This means a substantial loss of money for the utility companies and would have posed serious potential health risks if the software error had not been discovered. Seehttp: //www. 2. Incorrect assumptions can also be made in other ways. For example system developers may make incorrect assumptions about the requirements of certain modules.

For example one of the probes sent to Mars had the capability to have new mission requirements sent to it with instructions detailing what it was to do. After some time memory space on the probe in which new instructions could be stored, began to run out.

To remedy this one of the engineers decided to delete the landing modules (as the probe was never going to have to land again) and thus free up lots of storage space for new instructions. The probe was sent the new program overwriting the landing module. As soon as the new program was loaded into the probe computer, all contact with the probe was lost. What had happened was that the landing module needed celestial navigation information to land correctly. So the information needed to do this was part of the landing module, however the celestial navigation information was also needed to point the antenna that enabled communication with earth in the right direction. Therefore the loss of the landing module, caused the loss of the mars probe. Seehttp: //www. 2.

Poor user interface. A poor user interface may cause significant problems for users of the system and thus greatly increase the likelihood of those users introducing to the system data for example that causes system failure. In accounting software for example a single mistake due to poor user interface may cause an invoice to be sent to someone in the wrong currency or may turn what is supposed to be an invoice into a credit note. While not major problems, usually, data that is erroneous may make the system fail. General Accounting Office after reviewing for many years the federal computer systems has said, “data problems are primarily attributable to the user abuse of computer systems.” It then categorised the causes of these errors into six areas the first of which was “Forms designed and used for input preparation are too complex.” This information obtained from a book by William Perry shows that overly complex user interfaces is one of the biggest factors in data errors in the computer systems of the US government.

Faulty hardware. Faulty hardware is a problem that can cause severe system failure. It is also one that is hard to guard against. This factor is however an important one that should be given due consideration along with the more common software errors. An example of a hardware error in a system can be found in the experience of the Wide Field Infrared Explorer (WIRE) spacecraft operated by NASA. The failure of the system caused the WIRE to enter an uncontrolled 6. RPM spin within 4.

This dramatic failure of the system was due to faulty hardware components. See http: //catless. Risks/2. 0. 4. 7. Although not under the responsibility of the software developers faulty hardware should be taken into consideration when designing the systems so as to try and minimise the impact of the failure. Hardware failure is not as likely to occur as software faults but can be as damaging. Inadequate user training/ user error. This factor is an important contributor to system failure.

If a user is improperly trained then the likelihood of them making serious errors is increased due to their lack of knowledge of the system. Failures due to a lack of training should not be thought of as an error due to the individual operator as is likely with a poorly designed user interface, but as a mistake by the management. A person who makes a mistake should not be reprimanded for it if they have not been trained to deal with it. In the report by the U. S. General Accounting Office quoted by Perry (1. Poor fit between systems and organisation.

A poor fit between the system and the organisation can lead to various problems. A poor fit between a system and an organisation occurs when either the software developers or the entity requesting the software solution, do not grasp the full spectrum of tasks the new system will need to deal with. An example of this is the asylum system in this country, which is currently undergoing a major transformation in its system, there is a large shortfall between what is required by the system and what is actually available.