SOFTWARE TESTING Aarti Singh

Total Page:16

File Type:pdf, Size:1020Kb

SOFTWARE TESTING Aarti Singh © November 2015 | IJIRT | Volume 2 Issue 6 | ISSN: 2349-6002 SOFTWARE TESTING Aarti Singh ABSTRACT:- Software testing is an investigation can be installed and run in its conducted to provide stakeholders with intended environments, and information about the quality of the product or achieves the general result its stakeholders service under test Software testing can also desire. provide an objective, independent view of Software testing can be conducted as soon as thesoftware to allow the business to appreciate executable software (even if partially complete) and understand the risks of software exists. The overall approach to software implementation.In this paper we will discuss development often determines when and how testing about testing levels suct as alpha testing , beta is conducted. For example, in a phased process, most testing, unit testing , integrity testing, testing testing occurs after system requirements have been cycle and their requirement n comparison defined and then implemented in testable programs. between varius testing such as static and dynamic HISTORY: testing. The separation of debugging from testing was initially introduced by Glenford J. Myers in INTRODUCTION: 1979.[] Although his attention was on breakage Software testing involves the execution of a testing ("a successful test is one that finds a bug) it software component or system component to illustrated the desire of the software engineering evaluate one or more properties of interest. In community to separate fundamental development general, these properties indicate the extent to which activities, such as debugging, from that of the component or system under test: verification. Dave Gelperin and William C. meets the requirements that guided its Hetzel classified in 1988 the phases and goals in design and development, software testing in the following stages: responds correctly to all kinds of inputs, Until 1956 – Debugging oriented performs its functions within an acceptable 1957–1978 – Demonstration oriented time, 1979–1982 – Destruction oriented is sufficiently usable, 1983–1987 – Evaluation oriented 1988–2000 – Prevention oriented There are many approaches available in software testing. Reviews, walkthroughs, or inspections are TESTING METHODS: referred to asstatic testing, whereas actually executing programmed code with a given set of test cases is referred to as dynamic testing. Static testing Static vs. dynamic testing: is often implicit, as proofreading, plus when programming tools/text editors check source code IJIRT 142783 268 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY © November 2015 | IJIRT | Volume 2 Issue 6 | ISSN: 2349-6002 structure or compilers (pre-compilers) check syntax using stubs/drivers or execution from and data flow as static program analysis. Dynamic a debugger environment. testing takes place when the program itself is run. Static testing involves verification, whereas Dynamic testing may begin before the program is dynamic testing involves validation. Together they 100% complete in order to test particular sections of help improve software quality. Among the code and are applied to discrete functions or techniques for static analysis, mutation testing can modules. Typical techniques for this are either be used to ensure the test-cases will detect errors which are introduced by mutating the source code. statements in the program to be executed at White-box testing least once) White-box testing (also known as clear box Fault injection methods – intentionally testing, glass box testing, transparent box introducing faults to gauge the efficacy of testing and structural testing) tests internal testing strategies structures or workings of a program, as opposed to Mutation testing methods the functionality exposed to the end-user. In white- Static testing methods box testing an internal perspective of the system, as Code coverage tools can evaluate the completeness well as programming skills, are used to design test of a test suite that was created with any method, cases. The tester chooses inputs to exercise paths including black-box testing. This allows the through the code and determine the appropriate software team to examine parts of a system that are outputs. This is analogous to testing nodes in a rarely tested and ensures that the most circuit, e.g. in-circuit testing (ICT) important function points have been tested.[22] Code While white-box testing can be applied at coverage as a software metric can be reported as a the unit, integration and system levels of the percentage for: software testing process, it is usually done at the unit Function coverage, which reports on level. It can test paths within a unit, paths between functions executed units during integration, and between subsystems Statement coverage, which reports on the during a system–level test. Though this method of number of lines executed to complete the test design can uncover many errors or problems, it test might not detect unimplemented parts of the Decision coverage, which reports on specification or missing requirements. whether both the True and the False branch Techniques used in white-box testing include: of a given test has been executed API testing – testing of the application 100% statement coverage ensures that all code paths using public and private APIs (application or branches (in terms of control flow) are executed programming interfaces) at least once. This is helpful in ensuring correct Code coverage – creating tests to satisfy functionality, but not sufficient since the same code some criteria of code coverage (e.g., the may process different inputs correctly or incorrectly. test designer can create tests to cause all Black-box testing IJIRT 142783 269 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY © November 2015 | IJIRT | Volume 2 Issue 6 | ISSN: 2349-6002 descriptions of the software, including specifications, requirements, and designs to derive test cases. These tests can be functional or non- Black box functional, though usually functional. diagram Specification-based testing may be necessary to Black-box testing treats the software as a "black assure correct functionality, but it is insufficient to box", examining functionality without any guard against complex or high-risk situations.[25] knowledge of internal implementation. The testers One advantage of the black box technique is that no are only aware of what the software is supposed to programming knowledge is required. Whatever do, not how it does it.[23] Black-box testing methods biases the programmers may have had, the tester include: equivalence partitioning, boundary value likely has a different set and may emphasize analysis, all-pairs testing, state transition different areas of functionality. On the other hand, tables, decision table testing, fuzz testing, model- black-box testing has been said to be "like a walk in based testing, use case testing, exploratory a dark labyrinth without a flashlight."[26]Because testing and specification-based testing. they do not examine the source code, there are Specification-based testing aims to test the situations when a tester writes many test cases to functionality of software according to the applicable check something that could have been tested by only requirements.[24]This level of testing usually one test case, or leaves some parts of the program requires thorough test cases to be provided to the untested. tester, who then can simply verify that for a given This method of test can be applied to all levels of input, the output value (or behavior), either "is" or software "is not" the same as the expected value specified in testing: unit, integration, system and acceptance. It the test case. Test cases are built around typically comprises most if not all testing at higher specifications and requirements, i.e., what the levels, but can also dominate unit testing as well. application is supposed to do. It uses external recording of the entire test process – capturing everything that occurs on the test system in video Visual testing format. Output videos are supplemented by real- The aim of visual testing is to provide developers time tester input via picture-in-a-picture webcam with the ability to examine what was happening at and audio commentary from microphones. the point of software failure by presenting the data Visual testing provides a number of advantages. The in such a way that the developer can easily find the quality of communication is increased drastically information she or he requires, and the information because testers can show the problem (and the is expressed clearly.[27][28] events leading up to it) to the developer as opposed At the core of visual testing is the idea that showing to just describing it and the need to replicate test someone a problem (or a test failure), rather than just failures will cease to exist in many cases. The describing it, greatly increases clarity and developer will have all the evidence he or she understanding. Visual testing therefore requires the IJIRT 142783 270 INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY © November 2015 | IJIRT | Volume 2 Issue 6 | ISSN: 2349-6002 requires of a test failure and can instead focus on the test tool to visually record everything that occurs on cause of the fault and how it should be fixed. a system becomes very important in order to Visual testing is particularly well-suited for document the steps taken to uncover the bug.[Visual environments that deploy agile methods in
Recommended publications
  • The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models
    IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. XX, NO. XX, XXX 2016 1 The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models Feng Zhang, Ahmed E. Hassan, Member, IEEE, Shane McIntosh, Member, IEEE, and Ying Zou, Member, IEEE Abstract—Defect prediction models help software organizations to anticipate where defects will appear in the future. When training a defect prediction model, historical defect data is often mined from a Version Control System (VCS, e.g., Subversion), which records software changes at the file-level. Software metrics, on the other hand, are often calculated at the class- or method-level (e.g., McCabe’s Cyclomatic Complexity). To address the disagreement in granularity, the class- and method-level software metrics are aggregated to file-level, often using summation (i.e., McCabe of a file is the sum of the McCabe of all methods within the file). A recent study shows that summation significantly inflates the correlation between lines of code (Sloc) and cyclomatic complexity (Cc) in Java projects. While there are many other aggregation schemes (e.g., central tendency, dispersion), they have remained unexplored in the scope of defect prediction. In this study, we set out to investigate how different aggregation schemes impact defect prediction models. Through an analysis of 11 aggregation schemes using data collected from 255 open source projects, we find that: (1) aggregation schemes can significantly alter correlations among metrics, as well as the correlations between metrics and
    [Show full text]
  • Software Maintainability a Brief Overview & Application to the LFEV 2015
    Software Maintainability A Brief Overview & Application to the LFEV 2015 Adam I. Cornwell Lafayette College Department of Electrical & Computer Engineering Easton, Pennsylvania [email protected] Abstract — This paper gives a brief introduction on For “lexical level” approaches which base what software maintainability is, the various methods complexity on program code, the following which have been devised to quantify and measure software maintainability, its relevance to the ECE 492 measurands are typical: individual and average Senior Design course, and some practical lines of code; number of commented lines, implementation guidelines. executable statements, blank lines, tokens, and data declarations; source code readability, or the Keywords — Software Maintainability; Halstead ratio of LOC to commented LOC; Halstead Metrics; McCabe’s Cyclomatic complexity; radon Metrics, including Halstead Length, Volume, and Effort; McCabe’s Cyclomatic Complexity; the I. INTRODUCTION control structure nesting level; and the number of Software Maintainability is an important knots, or the number of times the control flow concept in the upkeep of long-term software crosses. The final measurand is not as useful with systems. According to the IEEE, software object oriented programming but can still be of maintainability is defined as “the ease with which some use. a software system or component can be modified to correct faults, improve performance or other “Psychological complexity” approaches measure attributes, or adapt to a changed environment difficulty and are based on understandability and [1].” Software Maintainability can be measured the user [3]. The nature of the file and the using various devised methods, although none difficulty experienced by those working with the have been conclusively shown to work in a large file are what contribute to these kinds of variety of software systems [6].
    [Show full text]
  • A Metrics-Based Software Maintenance Effort Model
    A Metrics-Based Software Maintenance Effort Model Jane Huffman Hayes Sandip C. Patel Liming Zhao Computer Science Department Computer Science Department Computer Science Department Lab for Advanced Networking University of Louisville University of Kentucky University of Kentucky [email protected] [email protected] [email protected] (corresponding author) Abstract planning a new software development project. Albrecht introduced the notion of function points (FP) to estimate We derive a model for estimating adaptive software effort [1]. Mukhopadhyay [19] proposes early software maintenance effort in person hours, the Adaptive cost estimation based on requirements alone. Software Maintenance Effort Model (AMEffMo). A number of Life Cycle Management (SLIM) [23] is based on the metrics such as lines of code changed and number of Norden/Rayleigh function and is suitable for large operators changed were found to be strongly correlated projects. Shepperd et al. [27] argued that algorithmic cost to maintenance effort. The regression models performed models such as COCOMO and those based on function well in predicting adaptive maintenance effort as well as points suggested an approach based on using analogous provide useful information for managers and maintainers. projects to estimate the effort for a new project. In addition to the traditional off-the-self models such as 1. Introduction COCOMO, machine-learning methods have surfaced recently. In [17], Mair et al. compared machine-learning Software maintenance typically accounts for at least 50 methods in building software effort prediction systems. percent of the total lifetime cost of a software system [16]. There has also been some work toward applying fuzzy Schach et al.
    [Show full text]
  • DRE) for Software
    Exceeding 99% in Defect Removal Efficiency (DRE) for Software Draft 11.0 September 6, 2016 Capers Jones, VP and CTO, Namcook Analytics LLC Abstract Software quality depends upon two important variables. The first variable is that of “defect potentials” or the sum total of bugs likely to occur in requirements, architecture, design, code, documents, and “bad fixes” or new bugs in bug repairs. Defect potentials are measured using function point metrics, since “lines of code” cannot deal with requirements and design defects. (This paper uses IFPUG function points version 4.3. The newer SNAP metrics are only shown experimentally due to insufficient empirical quality data with SNAP as of 2016. However an experimental tool is included for calculating SNAP defects.) The second important measure is “defect removal efficiency (DRE)” or the percentage of bugs found and eliminated before release of software to clients. The metrics of Defect Potentials and Defect Removal Efficiency (DRE) were developed by IBM circa 1973 and are widely used by technology companies and also by insurance companies, banks, and other companies with large software organizations. The author’s Software Risk Master (SRM) estimating tool predicts defect potentials and defect removal efficiency (DRE) as standard quality outputs for all software projects. Web: www.Namcook.com Email: [email protected] Copyright © 2016 by Capers Jones. All rights reserved. 1 Introduction Defect potentials and defect removal efficiency (DRE) are useful quality metrics developed by IBM circa 1973 and widely used by technology companies as well as by banks, insurance companies, and other organizations with large software staffs. This combination of defect potentials using function points and defect removal efficiency (DRE) are the only accurate and effective measures for software quality.
    [Show full text]
  • Empirical Evaluation of the Effectiveness and Reliability of Software Testing Adequacy Criteria and Reference Test Systems
    Empirical Evaluation of the Effectiveness and Reliability of Software Testing Adequacy Criteria and Reference Test Systems Mark Jason Hadley PhD University of York Department of Computer Science September 2013 2 Abstract This PhD Thesis reports the results of experiments conducted to investigate the effectiveness and reliability of ‘adequacy criteria’ - criteria used by testers to determine when to stop testing. The research reported here is concerned with the empirical determination of the effectiveness and reliability of both tests sets that satisfy major general structural code coverage criteria and test sets crafted by experts for testing specific applications. We use automated test data generation and subset extraction techniques to generate multiple tests sets satisfying widely used coverage criteria (statement, branch and MC/DC coverage). The results show that confidence in the reliability of such criteria is misplaced. We also consider the fault-finding capabilities of three test suites created by the international community to serve to assure implementations of the Data Encryption Standard (a block cipher). We do this by means of mutation analysis. The results show that not all sets are mutation adequate but the test suites are generally highly effective. The block cipher implementations are also seen to be highly ‘testable’ (i.e. they do not mask faults). 3 Contents Abstract ............................................................................................................................ 3 Table of Tables ...............................................................................................................
    [Show full text]
  • Lightweight Requirements Engineering Metrics Designing And
    Universiteit Leiden ICT in Business and the Public Sector Lightweight Requirements Engineering Metrics Designing and evaluating requirements engineering metrics for software development in complex IT environments – Case study Name: Jenny Yung Student-no: s0952249 Date: 18/04/2019 1st supervisor: Werner Heijstek 2nd supervisor: Guus Ramackers Pagina 1 van 83 Abstract The software development industry is relatively young and has matured considerably in the past 15 years. With several major transformations, the industry continues to seek ways to improve their delivery. A while ago, the waterfall method was the standard option for software development projects. Today, agile methodology is used more frequently. New development engineering practices are developed like standardization, continuous integration or continuous delivery. However, the consensus of being predictable and repeatable is not yet there. Agile software development and delivery are still not sufficiently reliable, resulting in expensive and unpredictable projects. In this thesis, measurements in requirements engineering are introduced. This paper presents a maturity model to assess the requirements engineering in complex IT environments. Companies strive to improve their software production and delivery, and the study aims to find a lightweight requirements metric method. The method is compared with a library of abstract metric models. The case study is performed at three different companies, and experts evaluated the conceptual metric system. As a result, a Requirements Engineering Maturity scan (REMS) is created. The tool suggests appropriate generic requirements metrics to deal with the encountered project. The paper concludes a review with related work and a discussion of the prospects for REMS. Keywords: Requirements Engineering • Quality Measurements • Metrics • ISO/IEC 9126 • Agile Methodology • Requirements Engineering Maturity Scan • Complex IT Infrastructure • Pagina 2 van 83 Acknowledgement Anyone writing a thesis will tell you it is a lot of work, and they are right.
    [Show full text]
  • Understanding ROI Metrics for Software Test Automation
    University of South Florida Scholar Commons Graduate Theses and Dissertations Graduate School 2005 Understanding ROI Metrics for Software Test Automation Naveen Jayachandran University of South Florida Follow this and additional works at: https://scholarcommons.usf.edu/etd Part of the American Studies Commons Scholar Commons Citation Jayachandran, Naveen, "Understanding ROI Metrics for Software Test Automation" (2005). Graduate Theses and Dissertations. https://scholarcommons.usf.edu/etd/2938 This Thesis is brought to you for free and open access by the Graduate School at Scholar Commons. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Scholar Commons. For more information, please contact [email protected]. Understanding ROI Metrics for Software Test Automation by Naveen Jayachandran A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science Department of Computer Science and Engineering College of Engineering University of South Florida Co-Major Professor: Dewey Rundus, Ph.D. Co-Major Professor: Alan Hevner, Ph.D. Member: Rafael Perez, Ph.D. Date of Approval: June 2, 2005 Keywords: Return on investment metrics, automated testing feasibility, manual vs. automated testing, software quality assurance, regression testing, repetitive testing © Copyright 2005, Naveen Jayachandran Dedication To my Parents, Suja, Rocky and Reemo. Acknowledgements I would like to thank my major professors Dr. Alan Hevner and Dr. Dewey Rundus for recognizing and encouraging my interest, giving me this opportunity and providing constant support for my research. I have gained immensely from this experience enabling me to truly appreciate the saying – ‘The journey is more important than the destination’.
    [Show full text]
  • 12 Steps to Useful Software Metrics
    12 Steps to Useful Software Metrics Linda Westfall The Westfall Team [email protected] PMB 101, 3000 Custer Road, Suite 270 Plano, TX 75075 972-867-1172 (voice) 972-943-1484 (fax) Abstract: 12 Steps to Useful Software Metrics introduces the reader to a practical process for establishing and tailoring a software metrics program that focuses on goals and information needs. The process provides a practical, systematic, start-to-finish method of selecting, designing and implementing software metrics. It outlines a cookbook method that the reader can use to simplify the journey from software metrics in concept to delivered information. Bio: Linda Westfall is the President of The Westfall Team, which provides Software Metrics and Software Quality Engineering training and consulting services. Prior to starting her own business, Linda was the Senior Manager of the Quality Metrics and Analysis at DSC Communications where her team designed and implemented a corporate wide metric program. Linda has more than twenty-five years of experience in real-time software engineering, quality and metrics. She has worked as a Software Engineer, Systems Analyst, Software Process Engineer and Manager of Production Software. Very active professionally, Linda Westfall is a past chair of the American Society for Quality (ASQ) Software Division. She has also served as the Software Division’s Program Chair and Certification Chair and on the ASQ National Certification Board. Linda is a past-chair of the Association for Software Engineering Excellence (ASEE) and has chaired several conference program committees. Linda Westfall has an MBA from the University of Texas at Dallas and BS in Mathematics from Carnegie- Mellon University.
    [Show full text]
  • Software Metrics: Investigating Success Factors, Challenges, Solutions and New Research Directions
    INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 9, ISSUE 08, AUGUST 2020 ISSN 2277-8616 Software Metrics: Investigating Success Factors, Challenges, Solutions And New Research Directions Hina Noor, Dr. Babur Hayat, AbuBakar Hamid, Tamoor Wakeel, Rashida Nasim Abstract: Software metrics is becoming important every day. A measure of software characteristics which are countable or measurable is known as software metrics. Software metrics are very important because they are used for many purposes for example, estimating efficiency, arranging work things and estimating execution of software etc. Software product and software development process specific attributes are measured by software metrics. Many metrics related to coupling, cohesion etc. have been defined. This paper investigates success factors and challenges in the implementation of software metrics and also tries to investigate solutions to those challenges. The paper explains new research directions to software metrics as well which really a need of today. Index Terms: Software metrics, Measurements, Types of metrics, Success factors of software metrics, Challenges in implementation of software metrics, New research directions in software metrics. —————————— —————————— 1. INTRODUCTION IN software, measurements are done using software metrics. A software metric is a measurement measure of the degree to which a software program or procedure has certain capital. Even if a metric is not a measure (metrics are functions, while measurements are the numbers obtained through metric application) [1]. Accurate measurement for all engineering fields is a requirement and software engineering is no different. For decades engineers and academics are persuading to communicate numerical software functionality to enable quality assurance of software. A broad variety of quality software measures have been created and there are various methods to obtain measures from system representations [2], [20].
    [Show full text]
  • Cyclomatic Complexity 6 Code Churn 8 Churn Vs
    www.codacy.com Index Why software metrics are important 3 Scope of this e-book 4 I. The Good 6 Cyclomatic complexity 6 Code churn 8 Churn vs. Complexity 9 Code coverage 10 Code duplication 12 Lack of Cohesion Of Methods (LCOM) 13 Relational cohesion 14 II. The Bad 15 Afferent & efferent coupling 15 Instability 17 Abstractness 18 Distance from main sequence (DMS) 19 III. The Ugly 21 Density of comments 21 Source line of code (SLOC) 22 Number of parameters 24 Number of variables 24 Number of overloads 24 Bonus: The best metric of all 25 Conclusion 25 About Codacy 26 Cheat sheet 27 Further reading 29 3 Why software metrics are important “If debugging is the process of removing software bugs, then programming must be the process of putting them in.” - Edsger Dijkstra Is the code tested? Is it readable? How easy is it to add or modify code without introducing bugs? Is it more complex than it needs to be? How maintainable is it going to be? Is it resource efficient? Is it easily extensible? Is technical debt creeping up? Software metrics are important because they help answer these questions. They provide objective, quantifiable measurements that support the myriad of decisions that developers make daily. Yet software metrics are not without their flaws, far from it. First, no single software metric can claim to provide a complete and accurate picture. Second, most metrics fail as universal yardsticks: sensitivity to factors such as programming language or even the lack of consensus on the algorithms that underpin them, lead to confusion and frustrations.
    [Show full text]
  • Software Metrics
    Software Metrics 1. Lord Kelvin, a physicist 2. George Miller, a psychologist � Software Metrics Product vs. process Most metrics are indirect: No way to measure property directly or Final product does not yet exist For predicting, need a model of relationship of predicted variable with other measurable variables. Three assumptions (Kitchenham) 1. We can accurately measure some property of software or process. 2. A relationship exists between what we can measure and what we want to know. 3. This relationship is understood, has been validated, and can be expressed in terms of a formula or model. Few metrics have been demonstrated to be predictable or related to product or process attributes. Software Metrics (2) . Code Static Dynamic Programmer productivity Design Testing Maintainability Management Cost Duration, time Staffing Code Metrics Estimate number of bugs left in code. From static analysis of code From dynamic execution Estimate future failure times: operational reliability Static Analysis of Code Halstead’s Software Physics or Software Science n1 = no. of distinct operators in program n2 = no. of distinct operands in program N1 = total number of operator occurrences N2 = total number of operand occurrences Program Length: N = N1 + N2 Program volume: V = N log (n1 + n2) 2 (represents the volume of information (in bits) necessary to specify a program.) Specification abstraction level: L = (2 * n2) / (n1 * N2) Program Effort: E = (n1 + N2 * (N1 + N2) * log (n1 + n2)) / (2 * n2) 2 (interpreted as number of mental discrimination required to implement the program.) McCabe’s Cyclomatic Complexity Hypothesis: Difficulty of understanding a program is largely determined by complexity of control flow graph.
    [Show full text]
  • The Mess of Software Metrics Version 10.0 May 4, 2017 Capers Jones, VP and CTO, Namcook Analytics LLC. Email
    The Mess of Software Metrics Version 10.0 May 4, 2017 Capers Jones, VP and CTO, Namcook Analytics LLC. www.Namcook.com Email: [email protected] Keywords: cost per defect, economic productivity, function points, lines of code (LOC), manufacturing economics, software productivity, SNAP metrics, software metrics, software quality, technical debt; function point metrics; function point 30th anniversary. Abstract The software industry is one of the largest, wealthiest, and most important industries in the modern world. The software industry is also troubled by very poor quality and very high cost structures due to the expense of software development, maintenance, and endemic problems with poor quality control. Accurate measurements of software development and maintenance costs and accurate measurement of quality would be extremely valuable. But as of 2017 the software industry labors under a variety of non-standard and highly inaccurate measures compounded by very sloppy measurement practices. For that matter, there is little empirical data about the efficacy of software standards themselves. The industry also lacks effective basic definitions for “software productivity” and “software quality” and uses a variety of ambiguous definitions that are difficult to predict before software is released and difficult to measure after the software is released. This paper suggests definitions for both economic software productivity and software quality that are both predictable and measurable. The year 2017 marks the 30th anniversary of function point metrics. Function point metrics are the best available for measuring software economic productivity and software quality. Copyright © 2016-2017 by Capers Jones. All rights reserved. 1 Introduction The software industry has become one of the largest and most successful industries in history.
    [Show full text]