Sunday, August 9, 2009

Software testing

Software Testing is an empirical investigation conducted to provide stakeholders with information about the quality of the product or service under test[1], with respect to the context in which it is intended to operate. Software Testing also provides an objective, independent view of the software to allow the business to appreciate and understand the risks at implementation of the software. Test techniques include, but are not limited to, the process of executing a program or application with the intent of finding software bugs. Software Testing can also be stated as the process of validating and verifying that a software program/application/product (1) meets the business and technical requirements that guided its design and development; (2) works as expected; and (3) can be implemented with the same characteristics.

Software Testing, depending on the testing method employed, can be implemented at any time in the development process, however most of the test effort occurs after the requirements have been defined and the coding process has been completed.

Contents

Overview

Testing can never completely identify all the defects within software. Instead, it furnishes a criticism or comparison that compares the state and behavior of the product against oracles—principles or mechanisms by which someone might recognize a problem. These oracles may include (but are not limited to) specifications, contracts[2], comparable products, past versions of the same product, inferences about intended or expected purpose, user or customer expectations, relevant standards, applicable laws, or other criteria.

Every software product has a target audience. For example, the audience for video game software is completely different from banking software. Therefore, when an organization develops or otherwise invests in a software product, it can assess whether the software product will be acceptable to its end users, its target audience, its purchasers, and other stakeholders. Software testing is the process of attempting to make this assessment.

A study conducted by NIST in 2002 reports that software bugs cost the U.S. economy $59.5 billion annually. More than a third of this cost could be avoided if better software testing was performed.[3]

History

The separation of debugging from testing was initially introduced by Glenford J. Myers in 1979.[4] Although his attention was on breakage testing ("a successful test is one that finds a bug"[4][5]), it illustrated the desire of the software engineering community to separate fundamental development activities, such as debugging, from that of verification. Dave Gelperin and William C. Hetzel classified in 1988 the phases and goals in software testing in the following stages:[6]

  • Until 1956 - Debugging oriented[7]
  • 1957–1978 - Demonstration oriented[8]
  • 1979–1982 - Destruction oriented[9]
  • 1983–1987 - Evaluation oriented[10]
  • 1988–2000 - Prevention oriented[11]

Software testing topics

Scope

A primary purpose for testing is to detect software failures so that defects may be uncovered and corrected. This is a non-trivial pursuit. Testing cannot establish that a product functions properly under all conditions but can only establish that it does not function properly under specific conditions.[12] The scope of software testing often includes examination of code as well as execution of that code in various environments and conditions as well as examining the aspects of code: does it do what it is supposed to do and do what it needs to do. In the current culture of software development, a testing organization may be separate from the development team. There are various roles for testing team members. Information derived from software testing may be used to correct the process by which software is developed[13].

Defects and failures

Not all software defects are caused by coding errors. One common source of expensive defects is caused by requirement gaps, e.g., unrecognized requirements, that result in errors of omission by the program designer[14]. A common source of requirements gaps is non-functional requirements such as testability, scalability, maintainability, usability, performance, and security.

Software faults occur through the following processes. A programmer makes an error (mistake), which results in a defect (fault, bug) in the software source code. If this defect is executed, in certain situations the system will produce wrong results, causing a failure.[15] Not all defects will necessarily result in failures. For example, defects in dead code will never result in failures. A defect can turn into a failure when the environment is changed. Examples of these changes in environment include the software being run on a new hardware platform, alterations in source data or interacting with different software.[15] A single defect may result in a wide range of failure symptoms.

Compatibility

A frequent cause of software failure is compatibility with another application, a new operating system, or, increasingly, web browser version. In the case of lack of backward compatibility, this can occur (for example...) because the programmers have only considered coding their programs for, or testing the software upon, "the latest version of" this-or-that operating system. The unintended consequence of this fact is that: their latest work might not be fully compatible with earlier mixtures of software/hardware, or it might not be fully compatible with another important operating system. In any case, these differences, whatever they might be, may have resulted in (unintended...) software failures, as witnessed by some significant population of computer users.

This could be considered a "prevention oriented strategy" that fits well with the latest testing phase suggested by Dave Gelperin and William C. Hetzel, as cited below [11].

Input combinations and preconditions

A very fundamental problem with software testing is that testing under all combinations of inputs and preconditions (initial state) is not feasible, even with a simple product.[12][16] This means that the number of defects in a software product can be very large and defects that occur infrequently are difficult to find in testing. More significantly, non-functional dimensions of quality (how it is supposed to be versus what it is supposed to do)—usability, scalability, performance, compatibility, reliability—can be highly subjective; something that constitutes sufficient value to one person may be intolerable to another.

Static vs. dynamic testing

There are many approaches to software testing. Reviews, walkthroughs, or inspections are considered as static testing, whereas actually executing programmed code with a given set of test cases is referred to as dynamic testing. Static testing can be (and unfortunately in practice often is) omitted. Dynamic testing takes place when the program itself is used for the first time (which is generally considered the beginning of the testing stage). Dynamic testing may begin before the program is 100% complete in order to test particular sections of code (modules or discrete functions). Typical techniques for this are either using stubs/drivers or execution from a debugger environment. For example, Spreadsheet programs are, by their very nature, tested to a large extent interactively ("on the fly"), with results displayed immediately after each calculation or text manipulation.

Software verification and validation

Software testing is used in association with verification and validation:[17]

  • Verification: Have we built the software right? (i.e., does it match the specification).
  • Validation: Have we built the right software? (i.e., is this what the customer wants).

The terms verification and validation are commonly used interchangeably in the industry; it is also common to see these two terms incorrectly defined. According to the IEEE Standard Glossary of Software Engineering Terminology:

Verification is the process of evaluating a system or component to determine whether the products of a given development phase satisfy the conditions imposed at the start of that phase.
Validation is the process of evaluating a system or component during or at the end of the development process to determine whether it satisfies specified requirements.

The software testing team

Software testing can be done by software testers. Until the 1980s the term "software tester" was used generally, but later it was also seen as a separate profession. Regarding the periods and the different goals in software testing[18], different roles have been established: manager, test lead, test designer, tester, automation developer, and test administrator.

Software Quality Assurance (SQA)

Though controversial, software testing may be viewed as an important part of the software quality assurance (SQA) process.[12] In SQA, software process specialists and auditors take a broader view on software and its development. They examine and change the software engineering process itself to reduce the amount of faults that end up in the delivered software: the so-called defect rate.

What constitutes an "acceptable defect rate" depends on the nature of the software. For example, an arcade video game designed to simulate flying an airplane would presumably have a much higher tolerance for defects than mission critical software such as that used to control the functions of an airliner that really is flying!

Although there are close links with SQA, testing departments often exist independently, and there may be no SQA function in some companies.

Software Testing is a task intended to detect defects in software by contrasting a computer program's expected results with its actual results for a given set of inputs. By contrast, QA (Quality Assurance) is the implementation of policies and procedures intended to prevent defects from occurring in the first place.

Testing methods

Approach of boxes

Software testing methods are traditionally divided into black box testing and white box testing. These two approaches are used to describe the point of view that a test engineer takes when designing test cases.

Black box testing

Black box testing treats the software as a "black box"—without any knowledge of internal implementation. Black box testing methods include: equivalence partitioning, boundary value analysis, all-pairs testing, fuzz testing, model-based testing, traceability matrix, exploratory testing and specification-based testing.

Specification-based testing: Specification-based testing aims to test the functionality of software according to the applicable requirements.[19] Thus, the tester inputs data into, and only sees the output from, the test object. This level of testing usually requires thorough test cases to be provided to the tester, who then can simply verify that for a given input, the output value (or behavior), either "is" or "is not" the same as the expected value specified in the test case.
Specification-based testing is necessary, but it is insufficient to guard against certain risks.[20]
Advantages and disadvantages: The black box tester has no "bonds" with the code, and a tester's perception is very simple: a code must have bugs. Using the principle, "Ask and you shall receive," black box testers find bugs where programmers do not. But, on the other hand, black box testing has been said to be "like a walk in a dark labyrinth without a flashlight," because the tester doesn't know how the software being tested was actually constructed. As a result, there are situations when (1) a tester writes many test cases to check something that could have been tested by only one test case, and/or (2) some parts of the back-end are not tested at all.

Therefore, black box testing has the advantage of "an unaffiliated opinion," on the one hand, and the disadvantage of "blind exploring," on the other. [21]

White box testing

White box testing is when the tester has access to the internal data structures and algorithms including the code that implement these.

Types of white box testing
The following types of white box testing exist:
  • API testing (application programming interface) - Testing of the application using Public and Private APIs
  • Code coverage - creating tests to satisfy some criteria of code coverage (e.g., the test designer can create tests to cause all statements in the program to be executed at least once)
  • Fault injection methods
  • Mutation testing methods
  • Static testing - White box testing includes all static testing
Code completeness evaluation
White box testing methods can also be used to evaluate the completeness of a test suite that was created with black box testing methods. This allows the software team to examine parts of a system that are rarely tested and ensures that the most important function points have been tested.[22]
Two common forms of code coverage are:
  • Function coverage, which reports on functions executed
  • Statement coverage, which reports on the number of lines executed to complete the test

They both return a code coverage metric, measured as a percentage.

Grey Box Testing

Grey box testing involves having access to internal data structures and algorithms for purposes of designing the test cases, but testing at the user, or black-box level. Manipulating input data and formatting output do not qualify as grey box, because the input and output are clearly outside of the "black-box" that we are calling the system under test. This distinction is particularly important when conducting integration testing between two modules of code written by two different developers, where only the interfaces are exposed for test. However, modifying a data repository does qualify as grey box, as the user would not normally be able to change the data outside of the system under test. Grey box testing may also include reverse engineering to determine, for instance, boundary values or error messages.

Integration Testing

Regression Testing

Acceptance testing

Acceptance testing can mean one of two things:

  1. A smoke test is used as an acceptance test prior to introducing a new build to the main testing process, i.e. before integration or regression.
  2. Acceptance testing performed by the customer, often in their lab environment on their own HW, is known as user acceptance testing (UAT).

Non Functional Software Testing

Special methods exist to test non-functional aspects of software.

In contrast to functional testing, which establishes the correct operation of the software (correct in that it matches the expected behavior defined in the design requirements), non-functional testing verifies that the software functions properly even when it receives invalid or unexpected inputs. Software fault injection, in the form of fuzzing, is an example of non-functional testing. Non-functional testing, especially for software, is designed to establish whether the device under test can tolerate invalid or unexpected inputs, thereby establishing the robustness of input validation routines as well as error-handling routines. Various commercial non-functional testing tools are linked from the Software fault injection page; there are also numerous open-source and free software tools available that perform non-functional testing..

Destructive testing

Destructive testing attempts to cause the software or a sub-system to fail, in order to test its robustness.

Testing process

A common practice of software testing is performed by an independent group of testers after the functionality is developed before it is shipped to the customer.[23] This practice often results in the testing phase being used as project buffer to compensate for project delays, thereby compromising the time devoted to testing.[24] Another practice is to start software testing at the same moment the project starts and it is a continuous process until the project finishes.[25]

In counterpoint, some emerging software disciplines such as extreme programming and the agile software development movement, adhere to a "test-driven software development" model. In this process, unit tests are written first, by the software engineers (often with pair programming in the extreme programming methodology). Of course these tests fail initially; as they are expected to. Then as code is written it passes incrementally larger portions of the test suites. The test suites are continuously updated as new failure conditions and corner cases are discovered, and they are integrated with any regression tests that are developed. Unit tests are maintained along with the rest of the software source code and generally integrated into the build process (with inherently interactive tests being relegated to a partially manual build acceptance process).

Testing can be done on the following levels:

  • Unit testing tests the minimal software component, or module. Each unit (basic component) of the software is tested to verify that the detailed design for the unit has been correctly implemented. In an object-oriented environment, this is usually at the class level, and the minimal unit tests include the constructors and destructors.[26]
  • Integration testing exposes defects in the interfaces and interaction between integrated components (modules). Progressively larger groups of tested software components corresponding to elements of the architectural design are integrated and tested until the software works as a system. [27]
  • System testing tests a completely integrated system to verify that it meets its requirements.[28]
  • System integration testing verifies that a system is integrated to any external or third party systems defined in the system requirements.[citation needed]

Before shipping the final version of software, alpha and beta testing are often done additionally:

  • Alpha testing is simulated or actual operational testing by potential users/customers or an independent test team at the developers' site. Alpha testing is often employed for off-the-shelf software as a form of internal acceptance testing, before the software goes to beta testing.[citation needed]
  • Beta testing comes after alpha testing. Versions of the software, known as beta versions, are released to a limited audience outside of the programming team. The software is released to groups of people so that further testing can ensure the product has few faults or bugs. Sometimes, beta versions are made available to the open public to increase the feedback field to a maximal number of future users.[citation needed]

Finally, acceptance testing can be conducted by the end-user, customer, or client to validate whether or not to accept the product. Acceptance testing may be performed as part of the hand-off process between any two phases of development.[citation needed]

Regression testing

After modifying software, either for a change in functionality or to fix defects, a regression test re-runs previously passing tests on the modified software to ensure that the modifications have not unintentionally caused a regression of previous functionality. Regression testing can be performed at any or all of the above test levels. These regression tests are often automated.

More specific forms of regression testing are known as sanity testing (which quickly checks for bizarre behavior) and smoke testing (which tests for basic functionality).

Benchmarks may be employed during regression testing to ensure that the performance of the newly modified software will be at least as acceptable as the earlier version or, in the case of code optimization, that some real improvement has been achieved.

Finding faults

Finding faults early

It is commonly believed that the earlier a defect is found the cheaper it is to fix it.[29] The following table shows the cost of fixing the defect depending on the stage it was found.[30] For example, if a problem in the requirements is found only post-release, then it would cost 10–100 times more to fix than if it had already been found by the requirements review.


Time Detected
Requirements Architecture Construction System Test Post-Release
Time Introduced Requirements 5–10× 10× 10–100×
Architecture - 10× 15× 25–100×
Construction - - 10× 10–25×

[edit] Testing Tools

Program testing and fault detection can be aided significantly by testing tools and debuggers. Testing/debug tools include features such as:

Some of these features may be incorporated into an Integrated Development Environment (IDE).

[edit] Measuring software testing

Usually, quality is constrained to such topics as correctness, completeness, security,[citation needed] but can also include more technical requirements as described under the ISO standard ISO 9126, such as capability, reliability, efficiency, portability, maintainability, compatibility, and usability.

There are a number of common software measures, often called "metrics", which are used to measure the state of the software or the adequacy of the testing.

[edit] Testing artifacts

Software testing process can produce several artifacts.

Test plan
A test specification is called a test plan. The developers are well aware what test plans will be executed and this information is made available to management and the developers. The idea is makes them more cautious when developing their code or making additional changes. Some companies have a higher-level document called a test strategy.
Traceability matrix
A traceability matrix is a table that correlates requirements or design documents to test documents. It is used to change tests when the source documents are changed, or to verify that the test results are correct.
Test case
A test case normally consists of a unique identifier, requirement references from a design specification, preconditions, events, a series of steps (also known as actions) to follow, input, output, expected result, and actual result. Clinically defined a test case is an input and an expected result.[31] This can be as pragmatic as 'for condition x your derived result is y', whereas other test cases described in more detail the input scenario and what results might be expected. It can occasionally be a series of steps (but often steps are contained in a separate test procedure that can be exercised against multiple test cases, as a matter of economy) but with one expected result or expected outcome. The optional fields are a test case ID, test step, or order of execution number, related requirement(s), depth, test category, author, and check boxes for whether the test is automatable and has been automated. Larger test cases may also contain prerequisite states or steps, and descriptions. A test case should also contain a place for the actual result. These steps can be stored in a word processor document, spreadsheet, database, or other common repository. In a database system, you may also be able to see past test results, who generated the results, and what system configuration was used to generate those results. These past results would usually be stored in a separate table.
Test script
The test script is the combination of a test case, test procedure, and test data. Initially the term was derived from the product of work created by automated regression test tools. Today, test scripts can be manual, automated, or a combination of both.
Test suite
The most common term for a collection of test cases is a test suite. The test suite often also contains more detailed instructions or goals for each collection of test cases. It definitely contains a section where the tester identifies the system configuration used during testing. A group of test cases may also contain prerequisite states or steps, and descriptions of the following tests.
Test data
In most cases, multiple sets of values or data are used to test the same functionality of a particular feature. All the test values and changeable environmental components are collected in separate files and stored as test data. It is also useful to provide this data to the client and with the product or a project.
Test harness
The software, tools, samples of data input and output, and configurations are all referred to collectively as a test harness.

[edit] A sample testing cycle

Although variations exist between organizations, there is a typical cycle for testing[32]:

  • Requirements analysis: Testing should begin in the requirements phase of the software development life cycle. During the design phase, testers work with developers in determining what aspects of a design are testable and with what parameters those tests work.
  • Test planning: Test strategy, test plan, testbed creation. Since many activities will be carried out during testing, a plan is needed.
  • Test development: Test procedures, test scenarios, test cases, test datasets, test scripts to use in testing software.
  • Test execution: Testers execute the software based on the plans and tests and report any errors found to the development team.
  • Test reporting: Once testing is completed, testers generate metrics and make final reports on their test effort and whether or not the software tested is ready for release.
  • Test result analysis: Or Defect Analysis, is done by the development team usually along with the client, in order to decide what defects should be treated, fixed, rejected (i.e. found software working properly) or deferred to be dealt with later.
  • Defect Retesting: Once a defect has been dealt with by the development team, it is retested by the testing team.
  • Regression testing: It is common to have a small test program built of a subset of tests, for each integration of new, modified, or fixed software, in order to ensure that the latest delivery has not ruined anything, and that the software product as a whole is still working correctly.
  • Test Closure: Once the test meets the exit criteria, the activities such as capturing the key outputs, lessons learned, results, logs, documents related to the project are archived and used as a reference for future projects.

[edit] Certifications

Several certification programs exist to support the professional aspirations of software testers and quality assurance specialists. No certification currently offered actually requires the applicant to demonstrate the ability to test software. No certification is based on a widely accepted body of knowledge. This has led some to declare that the testing field is not ready for certification.[33] Certification itself cannot measure an individual's productivity, their skill, or practical knowledge, and cannot guarantee their competence, or professionalism as a tester.[34]

Software testing certification types
  • Exam-based: Formalized exams, which need to be passed; can also be learned by self-study [e.g., for ISTQB or QAI]
  • Education-based: Instructor-led sessions, where each course has to be passed [e.g., International Institute for Software Testing (IIST)].
Testing certifications
Quality assurance certifications

[edit] Controversy

Some of the major software testing controversies include:

What constitutes responsible software testing?
Members of the "context-driven" school of testing[43] believe that there are no "best practices" of testing, but rather that testing is a set of skills that allow the tester to select or invent testing practices to suit each unique situation. [44]
Agile vs. traditional
Should testers learn to work under conditions of uncertainty and constant change or should they aim at process "maturity"? The agile testing movement has received growing popularity since 2006 mainly in commercial circles [45], whereas government and military software providers are slow to embrace this methodology[neutrality disputed], and mostly still hold to CMMI.[46]
Exploratory test vs. scripted[47]
Should tests be designed at the same time as they are executed or should they be designed beforehand?
Manual testing vs. automated
Some writers believe that test automation is so expensive relative to its value that it should be used sparingly.[48] Others, such as advocates of agile development, recommend automating 100% of all tests.[citation needed] More in particular, test-driven development states that developers should write unit-tests of the XUnit type before coding the functionality. The tests then can be considered as a way to capture and implement the requirements.
Software design vs. software implementation[49]
Should testing be carried out only at the end or throughout the whole process?
Who watches the watchmen?
The idea is that any form of observation is also an interaction—the act of testing can also affect that which is being tested[50]

Software design

Software design is a process of problem-solving and planning for a software solution. After the purpose and specifications of software are determined, software developers will design or employ designers to develop a plan for a solution. It includes low-level component and algorithm implementation issues as well as the architectural view.

Contents

Overview

The software requirements analysis (SRA) step of a software development process yields specifications that are used in software engineering. If the software is "semiautomated" or user centered, software design may involve user experience design yielding a story board to help determine those specifications. If the software is completely automated (meaning no user or user interface), a software design may be as simple as a flow chart or text describing a planned sequence of events. There are also semi-standard methods like Unified Modeling Language and Fundamental modeling concepts. In either case some documentation of the plan is usually the product of the design.

A software design may be platform-independent or platform-specific, depending on the availability of the technology called for by the design.

Software design topics

Design Concepts

The design concepts provide the software designer with a foundation from which more sophisticated methods can be applied. A set of fundamental design concepts has evolved. They are:

  • 1.Abstraction - Abstraction is the process or result of generalization by reducing the information content of a concept or an observable phenomenon, typically in order to retain only information which is relevant for a particular purpose.
  • 2.Refinement - It is the process of elaboration. A hierarchy is developed by decomposing a macroscopic statement of function in a stepwise fashion until programming language statements are reached. In each step, one or several instructions of a given program are decomposed into more detailed instructions. Abstraction and Refinement are complementary concepts.
  • 3.Modularity - Software architecture is divided into components called modules.
  • 4.Software Architecture - It refers to the overall structure of the software and the ways in which that structure provides conceptual integrity for a system. A software architecture is the development work product that gives the highest return on investment with respect to quality, schedule and cost.
  • 5.Control Hierarchy - A program structure that represent the organization of a program components and implies a hierarchy of control.
  • 6.Structural Partitioning - The program structure can be divided both horizontally and vertically. Horizontal partitions define separate branches of modular hierarchy for each major program function. Vertical partitioning suggests that control and work should be distributed top down in the program structure.
  • 7.Data Structure - It is a representation of the logical relationship among individual elements of data.
  • 8.Software Procedure - It focuses on the processing of each modules individually
  • 9.Information Hiding - Modules should be specified and designed so that information contained within a module is inaccessible to other modules that have no need for such information.

Design considerations

There are many aspects to consider in the design of a piece of software. The importance of each should reflect the goals the software is trying to achieve. Some of these aspects are:

  • Compatibility - The software is able to operate with other products that are designed for interoperability with another product. For example, a piece of software may be backward-compatible with an older version of itself.
  • Extensibility - New capabilities can be added to the software without major changes to the underlying architecture.
  • Fault-tolerance - The software is resistant to and able to recover from component failure.
  • Maintainability - The software can be restored to a specified condition within a specified period of time. For example, antivirus software may include the ability to periodically receive virus definition updates in order to maintain the software's effectiveness.
  • Modularity - the resulting software comprises well defined, independent components. That leads to better maintainability. The components could be then implemented and tested in isolation before being integrated to form a desired software system. This allows division of work in a software development project.
  • Packaging - Printed material such as the box and manuals should match the style designated for the target market and should enhance usability. All compatibility information should be visible on the outside of the package. All components required for use should be included in the package or specified as a requirement on the outside of the package.
  • Reliability - The software is able to perform a required function under stated conditions for a specified period of time.
  • Reusability - the modular components designed should capture the essence of the functionality expected out of them and no more or less. This single-minded purpose renders the components reusable wherever there are similar needs in other designs.
  • Robustness - The software is able to operate under stress or tolerate unpredictable or invalid input. For example, it can be designed with a resilience to low memory conditions.
  • Security - The software is able to withstand hostile acts and influences.
  • Usability - The software user interface must be intuitive (and often aesthetically pleasing) to its target user/audience. Default values for the parameters must be chosen so that they are a good choice for the majority of the users. In many cases, online help should be included and also carefully designed.

Modeling language

A modeling language is any artificial language that can be used to express information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure. A modeling language can be graphical or textual. Examples of graphical modelling languages for software design are:

Design patterns

A software designer or architect may identify a design problem which has been solved by others before. A template or pattern describing a solution to a common problem is known as a design pattern. The reuse of such patterns can speed up the software development process, having been tested and proved in the past.

Usage

Software design documentation may be reviewed or presented to allow constraints, specifications and even requirements to be adjusted prior to programming. Redesign may occur after review of a programmed simulation or prototype. It is possible to design software in the process of programming, without a plan or requirement analysis, but for more complex projects this would not be considered a professional approach. A separate design prior to programming allows for multidisciplinary designers and Subject Matter Experts (SMEs) to collaborate with highly-skilled programmers for software that is both useful and technically sound.

Software Sizing

Software sizing is an important activity in software engineering that is used to estimate the size of a software application or component in order to be able to implement other software project management activities (such as estimating or tracking). Size is an inherent characteristic of a software in just like weight is an inherent characteristic of any tangible material.

Contents

Background

It is essential to differentiate between software sizing and software effort estimation. Measuring the size of a piece of software is different from measuring the effort needed to build it. We need to measure the size of software in order to be able to measure productivity.

Example

For example, if a software engineer has built a small web-based calculator application, we can say that the project effort size was 280 man-hours. However, this does not give any information about the size of the software product itself. Conversely, we can say that the application size is 5,000 LOCs (Lines Of Code),ff or 30 FPs(Function Points).

Software Sizing Methods

Historically, the most common software sizing methodology was counting the lines of code written in the application source. Although this method is easy and straightforward, it is no longer practical due to the great advancements in software engineering and modern programming languages. Another commonly used sizing method is the IFPUG method called Function point analysis. The IFPUG FPA functional sizing method(FSM) has proven successful and accurate for more than thirty years. However, its accuracy and effectiveness has lately become highly controversial. Reasons behind the criticism of FP analysis include lack of sensitivity towards algorithmic complexity and its relative difficulty.

New trends of software sizing have recently emerged. For example, Use Case based software sizing relies on counting the number and characteristics of Use Cases found in a piece of software. Also, different variations of Function Points have emerged over the years, such as Object Oriented Function Points, or OOFP. Other methods such as COSMIChave evolved to address sizing software that has a very limited amound of stored data such as 'process control' and 'real time' systems. Both the IFPUG Method and the COSMIC Method are ISO/IEC standards. For more information about the similarities and differences between these ISO FSM methods see IFPUG and COSMIC - Similarities and Differences. In order to determine the best Functional Sizing Method for your software you need to consider a number of factors, including the functional domain of your applications, the process maturity of your organisation and the extent of use of the FSM Method. For more guidelines on how to choose an FSM Method[1] see ISO/IEC 14143-6: - SOFTWARE ENGINEERING — SOFTWARE MEASUREMENT — FUNCTIONAL SIZE MEASUREMENT — PART 6: GUIDE FOR USE OF ISO/IEC 14143 SERIES AND RELATED INTERNATIONAL STANDARDS. There are many uses and benefits of function points[2] beyond measuring project productivity and estimating planned projects, these include monitoring project progress and evaluating the requirements coverage of COTS packages.

COCOMO Model

The COnstructive COst MOdel (COCOMO) is an algorithmic Software Cost Estimation Model developed by Barry Boehm. The model uses a basic regression formula, with parameters that are derived from historical project data and current project characteristics.

COCOMO was first published in 1981 Barry W. Boehm's Book Software engineering economics[1] as a model for estimating effort, cost, and schedule for software projects. It drew on a study of 63 projects at TRW Aerospace where Barry Boehm was Director of Software Research and Technology in 1981. The study examined projects ranging in size from 2,000 to 100,000 lines of code, and programming languages ranging from assembly to PL/I. These projects were based on the waterfall model of software development which was the prevalent software development process in 1981.

References to this model typically call it COCOMO 81. In 1997 COCOMO II was developed and finally published in 2001 in the book Software Cost Estimation with COCOMO II[2]. COCOMO II is the successor of COCOMO 81 and is better suited for estimating modern software development projects. It provides more support for modern software development processes and an updated project database. The need for the new model came as software development technology moved from mainframe and overnight batch processing to desktop development, code reusability and the use of off-the-shelf software components. This article refers to COCOMO 81.

COCOMO consists of a hierarchy of three increasingly detailed and accurate forms. The first level, Basic COCOMO is good for quick, early, rough order of magnitude estimates of software costs, but its accuracy is limited due to its lack of factors to account for difference in project attributes (Cost Drivers). Intermediate COCOMO takes these Cost Drivers into account and Detailed COCOMO additionally accounts for the influence of individual project phases.

Contents

Basic COCOMO

Basic COCOMO computes software development effort (and cost) as a function of program size. Program size is expressed in estimated thousands of lines of code (KLOC).

COCOMO applies to three classes of software projects:

  • Organic projects - "small" teams with "good" experience working with "less than rigid" requirements
  • Semi-detached projects - "medium" teams with mixed experience working with a mix of rigid and less than rigid requirements
  • Embedded projects - developed within a set of "tight" constraints (hardware, software, operational, ...)

The basic COCOMO equations take the form

Effort Applied = ab(KLOC)bb [ man-months ]
Development Time = cb(Effort Applied)db [months]
People required = Effort Applied / Development Time [count]

The coefficients ab, bb, cb and db are given in the following table.

   Software project    ab      bb      cb      db

Organic 2.4 1.05 2.5 0.38
Semi-detached 3.0 1.12 2.5 0.35
Embedded 3.6 1.20 2.5 0.32

Basic COCOMO is good for quick estimate of software costs. However it does not account for differences in hardware constraints, personnel quality and experience, use of modern tools and techniques, and so on.

Intermediate COCOMO

Intermediate COCOMO computes software development effort as function of program size and a set of "cost drivers" that include subjective assessment of product, hardware, personnel and project attributes. This extension considers a set of four "cost drivers", each with a number of subsidiary attributes:-

  • Product attributes
    • Required software reliability
    • Size of application database
    • Complexity of the product
  • Hardware attributes
    • Run-time performance constraints
    • Memory constraints
    • Volatility of the virtual machine environment
    • Required turnabout time
  • Personnel attributes
    • Analyst capability
    • Software engineering capability
    • Applications experience
    • Virtual machine experience
    • Programming language experience
  • Project attributes
    • Use of software tools
    • Application of software engineering methods
    • Required development schedule

Each of the 15 attributes receives a rating on a six-point scale that ranges from "very low" to "extra high" (in importance or value). An effort multiplier from the table below applies to the rating. The product of all effort multipliers results in an effort adjustment factor (EAF). Typical values for EAF range from 0.9 to 1.4.

Cost Drivers Ratings
Very Low Low Nominal High Very High Extra High
Product attributes
Required software reliability 0.75 0.88 1.00 1.15 1.40
Size of application database 0.94 1.00 1.08 1.16
Complexity of the product 0.70 0.85 1.00 1.15 1.30 1.65
Hardware attributes
Run-time performance constraints 1.00 1.11 1.30 1.66
Memory constraints 1.00 1.06 1.21 1.56
Volatility of the virtual machine environment 0.87 1.00 1.15 1.30
Required turnabout time 0.87 1.00 1.07 1.15
Personnel attributes
Analyst capability 1.46 1.19 1.00 0.86 0.71
Applications experience 1.29 1.13 1.00 0.91 0.82
Software engineer capability 1.42 1.17 1.00 0.86 0.70
Virtual machine experience 1.21 1.10 1.00 0.90
Programming language experience 1.14 1.07 1.00 0.95
Project attributes
Use of software tools 1.24 1.10 1.00 0.91 0.82
Application of software engineering methods 1.24 1.10 1.00 0.91 0.83
Required development schedule 1.23 1.08 1.00 1.04 1.10

The Intermediate Cocomo formula now takes the form:

E=ai(KLoC)(bi).EAF

where E is the effort applied in person-months, KLoC is the estimated number of thousands of delivered lines of code for the project, and EAF is the factor calculated above. The coefficient ai and the exponent bi are given in the next table.

Software project ai bi
Organic 3.2 1.05
Semi-detached 3.0 1.12
Embedded 2.8 1.20

The Development time D calculation uses E in the same way as in the Basic COCOMO.

[edit] Detailed COCOMO

Detailed COCOMO - incorporates all characteristics of the intermediate version with an assessment of the cost driver's impact on each step (analysis, design, etc.) of the software engineering process.