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ASSESSING the MAINTAINABILITY of C++ SOURCE CODE by MARIUS SUNDBAKKEN a Thesis Submitted in Partial Fulfillment of the Requireme
ASSESSING THE MAINTAINABILITY OF C++ SOURCE CODE By MARIUS SUNDBAKKEN A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science WASHINGTON STATE UNIVERSITY School of Electrical Engineering and Computer Science DECEMBER 2001 To the Faculty of Washington State University: The members of the Committee appointed to examine the thesis of MARIUS SUNDBAKKEN find it satisfactory and recommend that it be accepted. Chair ii ASSESSING THE MAINTAINABILITY OF C++ SOURCE CODE Abstract by Marius Sundbakken, M.S. Washington State University December 2001 Chair: David Bakken Maintenance refers to the modifications made to software systems after their first release. It is not possible to develop a significant software system that does not need maintenance because change, and hence maintenance, is an inherent characteristic of software systems. It has been estimated that it costs 80% more to maintain software than to develop it. Clearly, maintenance is the major expense in the lifetime of a software product. Predicting the maintenance effort is therefore vital for cost-effective design and development. Automated techniques that can quantify the maintainability of object- oriented designs would be very useful. Models based on metrics for object-oriented source code are necessary to assess software quality and predict engineering effort. This thesis will look at C++, one of the most widely used object-oriented programming languages in academia and industry today. Metrics based models that assess the maintainability of the source code using object-oriented software metrics are developed. iii Table of Contents 1. Introduction .................................................................................................................1 1.1. Maintenance and Maintainability....................................................................... -
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle Fayssai M. Safie, Ph. D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Fayssal.Safie @ msfc.nasa.gov Charles Daniel, Ph.D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Charles.Daniel @msfc.nasa.gov Prince Kalia Raytheon ITSS Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-6871 E-mail: Prince.Kalia @ msfc.nasa.gov ABSTRACT The United States National Aeronautics and Space Administration (NASA) is in the midst of a 10-year Second Generation Reusable Launch Vehicle (RLV) program to improve its space transportation capabilities for both cargo and crewed missions. The objectives of the program are to: significantly increase safety and reliability, reduce the cost of accessing low-earth orbit, attempt to leverage commercial launch capabilities, and provide a growth path for manned space exploration. The safety, reliability and life cycle cost of the next generation vehicles are major concerns, and NASA aims to achieve orders of magnitude improvement in these areas. To get these significant improvements, requires a rigorous process that addresses Reliability, Maintainability and Supportability (RMS) and safety through all the phases of the life cycle of the program. This paper discusses the RMS process being implemented for the Second Generation RLV program. 1.0 INTRODUCTION The 2nd Generation RLV program has in place quantitative Level-I RMS, and cost requirements [Ref 1] as shown in Table 1, a paradigm shift from the Space Shuttle program. This paradigm shift is generating a change in how space flight system design is approached. -
Software Quality Management
Software Quality Management 2004-2005 Marco Scotto ([email protected]) Software Quality Management Contents ¾Definitions ¾Quality of the software product ¾Special features of software ¾ Early software quality models •Boehm model • McCall model ¾ Standard ISO 9126 Software Quality Management 2 Definitions ¾ Software: intellectual product consisting of information stored on a storage device (ISO/DIS 9000: 2000) • Software may occur as concepts, transactions, procedures. One example of software is a computer program • Software is "intellectual creation comprising the programs, procedures, rules and any associated documentation pertaining to the operation of a data processing system" •A software product is the "complete set of computer programs, procedures and associated documentation and data designated for delivery to a user" [ISO 9000-3] • Software is independent of the medium on which it is recorded Software Quality Management 3 Quality of the software product ¾The product should, on the highest level… • Ensure the satisfaction of the user needs • Ensure its proper use ¾ Earlier: 1 developer, 1 user • The program should run and produce results similar to those expected ¾ Later: more developers, more users • Need to economical use of the storage devices • Understandability, portability • User-friendliness, learnability ¾ Nowadays: • Efficiency, reliability, no errors, able to restart without using data Software Quality Management 4 Special features of software (1/6) ¾ Why is software ”different”? • Does not really have “physical” existence -
Software Maintainability and Usability in Agile Environment
Software Maintainability and Usability in Agile Environment {tag} {/tag} International Journal of Computer Applications © 2013 by IJCA Journal Volume 68 - Number 4 Year of Publication: 2013 Authors: Monika Agarwal Rana Majumdar 10.5120/11569-6873 {bibtex}pxc3886873.bib{/bibtex} Abstract This research is based on software maintainability and usability in the agile environment. Maintainability of the system is the ability to undergo changes relatively easily. These changes can affect components, services, interfaces and functionality when adding or changing functions, errors, and respond to business needs. Usability is defined as the application that meets the requirements of users and consumers by providing an intuitive, easy to locate and globalize and provides good access for disabled users and leads to a good overall user experience. In the conventional method of the software development, there are many metrics to calculate the maintenance and use of software. This research is to determine whether the same measures apply to Agile, or there is a need to change some metrics used for the agile environment. The goal of software engineering is to develop good quality maintainable software in schedule and budget. Inflated software costing, delayed time frame, or not meeting quality standards express a failure. A survey suggests about 45% of software fails due to the lack of quality. It is therefore one of the most important aspects for the success of software. Refer ences 1 / 3 Software Maintainability and Usability in Agile Environment - P. Antonellis, D. Antoniou, Y. Kanellopoulos, C. Makris, E. Theodoridis, C. Tjortjis, and N. Tsirakis, "A data mining methodology for evaluating maintainability according to ISO/IEC-9126 software engineering – product quality standard," in Special Session on System Quality and Maintainability - SQM2007, 2007. -
Adaptability Evaluation at Software Architecture Level Pentti Tarvainen*
The Open Software Engineering Journal, 2008, 2, 1-30 1 Open Access Adaptability Evaluation at Software Architecture Level Pentti Tarvainen* VTT Technical Research Centre of Finland, Kaitoväylä 1, P.O. Box 1100, FIN-90571 Oulu, Finland Abstract: Quality of software is one of the major issues in software intensive systems and it is important to analyze it as early as possible. An increasingly important quality attribute of complex software systems is adaptability. Software archi- tecture for adaptive software systems should be flexible enough to allow components to change their behaviors depending upon the environmental and stakeholders' changes and goals of the system. Evaluating adaptability at software architec- ture level to identify the weaknesses of the architecture and further to improve adaptability of the architecture are very important tasks for software architects today. Our contribution is an Adaptability Evaluation Method (AEM) that defines, before system implementation, how adaptability requirements can be negotiated and mapped to the architecture, how they can be represented in architectural models, and how the architecture can be evaluated and analyzed in order to validate whether or not the requirements are met. AEM fills the gap from requirements engineering to evaluation and provides an approach for adaptability evaluation at the software architecture level. In this paper AEM is described and validated with a real-world wireless environment control system. Furthermore, adaptability aspects, role of quality attributes, and diversity of adaptability definitions at software architecture level are discussed. Keywords: Adaptability, adaptation, adaptive software architecture, software quality, software quality attribute. INTRODUCTION understand the system [6]. Examples of design decisions are the decisions such as “we shall separate user interface from Today, quality of a software system plays an increasingly the rest of the application to make both user interface and important role in the domain of software engineering. -
Software Maintainability and Reusability Using Cohesion Metrics
International Journal of Computer Trends and Technology (IJCTT) – Volume 54 Issue 2-December2017 Software Maintainability and Reusability using Cohesion Metrics Adekola, O.D#1, Idowu, S.A*2, Okolie, S.O#3, Joshua, J.V#4, Akinsanya, A.O*5, Eze, M.O#6, EbiesuwaSeun#7 #1Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria *2Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria #3Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria #4Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria *5Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria #6Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria #7Faculty, Computer Science Department, Babcock University,Ilishan-Remo, Ogun State, Nigeria Abstract - Among others, remarkable external software’s lifetime. Ahn et al., (2003) estimated that quality attributes of interest to software practitioners/ maintenance takes up to 80% of the total costof engineers include testability, maintainability and producing software applications. Expectation of reusability.Software engineers still combat achieving more reliable, quicker time-to-market and softwarecrisis and even chronic software affliction maintainable systems. A lot of research has gone into not because there is no standardized software the areas of software reuse and maintenance due to development process but because enough attention is the fact that these among other issues concern not given to seemingly insignificant but crucial intimately system developers/architects/engineers details of internal design attributes such as cohesion rather than end-users. Therehas been enormous and coupling especially in object-oriented systems. growth in software reuse research from the days of Consequently, the aftermath is increased structured programming concepts to object-oriented maintenance cost, effort and time which negatively methods and beyond (e.g. -
Measuring Software Maintainability
Aalto University School of Science Degree Programme in Computer Science and Engineering Juha Viljanen Measuring software maintainability Master's Thesis Espoo, August 10, 2015 Supervisor: Professor Marjo Kauppinen, Aalto University Advisor: Robert Brotherus Lic.Phil. (Chem.) Timo Lehtinen D.Sc. (Tech) Aalto University School of Science ABSTRACT OF Degree Programme in Computer Science and Engineering MASTER'S THESIS Author: Juha Viljanen Title: Measuring software maintainability Date: August 10, 2015 Pages: 94 Major: Software Engineering Code: T241-3 Supervisor: Professor Marjo Kauppinen Advisor: Robert Brotherus Lic.Phil. (Chem.) Timo Lehtinen D.Sc. (Tech) Maintenance forms the major part of the software's development costs. Keep- ing the software's code base in a good shape, i.e. maintainable, speeds up and reduces the cost of making changes and makes them less risky. Refactoring and reengineering are practices to improve a software's maintainability. An essential part of keeping a software's maintainability at a desired level or systematically improving its maintainability is to measure the progress and to find the parts of code whose improvement would have the biggest affect on the overall maintainability. This can be done by using metrics. It is crucial to make sure the metrics are actively used by the company personnel. This can be done by providing useful deployment practices for the metrics. A literature review and a case study was conducted using a simplified version of the Design Science. The aim was to find a relevant set of metrics and useful ways of deploying them at the case company. In the case study, workshops were organized at the case company to fulfill the aim. -
Introduction to Statistically Designed Experiments, NREL
Introduction to Statistically Designed Experiments Mike Heaney ([email protected]) ASQ Denver Section 1300 Monthly Membership Meeting September 13, 2016 Englewood, Colorado NREL/PR-5500-66898 Objectives Give some basic background information on statistically designed experiments Demonstrate the advantages of using statistically designed experiments1 1 Often referred to as design of experiments (DOE or DOX), or experimental design. 2 A Few Thoughts Many companies/organizations do experiments for Process improvement Product development Marketing strategies We need to plan our experiments as efficiently and effectively as possible Statistically designed experiments have a proven track record (Mullin 2013 in Chemical & Engineering News) Conclusions supported by statistical tests Substantial reduction in total experiments Why are we still planning experiments like it’s the 19th century? 3 Outline • Research Problems • The Linear Model • Key Types of Designs o Full Factorial o Fractional Factorial o Response Surface • Sequential Approach • Summary 4 Research Problems Research Problems Is a dependent variable (Y) affected by multiple independent variables (Xs)? Objective: Increase bearing lifetime (Hellstrand 1989) Y Bearing lifetime (hours) X1 Inner ring heat treatment Outer ring osculation (ratio Outer Ring X2 between ball diameter and outer ring raceway radius) Ball X3 Cage design Cage Inner Ring 6 Research Problems Objective: Hydrogen cyanide (HCN) removal in diesel exhaust gas (Zhao et al. 2006) Y HCN conversion X1 Propene -
Appendix A: Mathematical Formulas
Appendix A: Mathematical Formulas Logarithms Inab = \na -\-\nb', \na/b = \na— \nb; \na^=b\na Geometric Series If Irl < 1, we find that oo oo E ar^ = ; y^akr^ = r; ^=0 A:=o y^ n Z-^ 1 -r ^^ 1 -r ^=1 k=0 Limits UHospitaVs rule. Suppose that \\vOix->XQ fix) = lim;c-^A:o sM = 0* or that liirix-^xo Z^-^) = l™x^jco SM — =t^^- Then, under some conditions, we can write that y fix) ^. fix) um = hm . x-^XQ g{x) x^xo g^{x) If the functions f\x) and g\x) satisfy the same conditions as f(x) and g(x), we can repeat the process. Moreover, the constant XQ may be equal to ±oo. Derivatives Derivative of a quotient: d fix) gix)fix) - fix)g\x) = 7^ 11 gix) ^0. dx gix) g^ix) 340 Appendix A: Mathematical Formulas Remark. This formula can also be obtained by differentiating the product f(x)h(x), where/Z(A:) := l/g(x). Chain rule. If w = ^(jc), then we have: d d du , , ^fiu) = —f(u) . — = f\u)g\x) = AgixWix). dx du dx For example, if w = jc^, we calculate — exp(M^) = exp(w^)2w • 2x = 4x^ expU"*). dx Integrals Integration by parts: j udv = uv — I vdu. Integration by substitution. If the inverse function g~^ exists, then we can write that rh rd j f(x)dx = j f[g(y)]g{y)dy, where a = g(c) ^ c = g ^(a) and b = g(d) <^ d = g ^(b). For example, / e''^'dx-^=^' f 2yeydy=2yey\l-2 f e^ dy = 2e^-\-2. -
STATISTICAL SCIENCE Volume 31, Number 4 November 2016
STATISTICAL SCIENCE Volume 31, Number 4 November 2016 ApproximateModelsandRobustDecisions..................James Watson and Chris Holmes 465 ModelUncertaintyFirst,NotAfterwards....................Ingrid Glad and Nils Lid Hjort 490 ContextualityofMisspecificationandData-DependentLosses..............Peter Grünwald 495 SelectionofKLNeighbourhoodinRobustBayesianInference..........Natalia A. Bochkina 499 IssuesinRobustnessAnalysis.............................................Michael Goldstein 503 Nonparametric Bayesian Clay for Robust Decision Bricks ..................................................Christian P. Robert and Judith Rousseau 506 Ambiguity Aversion and Model Misspecification: An Economic Perspective ................................................Lars Peter Hansen and Massimo Marinacci 511 Rejoinder: Approximate Models and Robust Decisions. .James Watson and Chris Holmes 516 Filtering and Tracking Survival Propensity (Reconsidering the Foundations of Reliability) .......................................................................Nozer D. Singpurwalla 521 On Software and System Reliability Growth and Testing. ..............FrankP.A.Coolen 541 HowAboutWearingTwoHats,FirstPopper’sandthendeFinetti’s?..............Elja Arjas 545 WhatDoes“Propensity”Add?...................................................Jane Hutton 549 Reconciling the Subjective and Objective Aspects of Probability ...............Glenn Shafer 552 Rejoinder:ConcertUnlikely,“Jugalbandi”Perhaps..................Nozer D. Singpurwalla 555 ChaosCommunication:ACaseofStatisticalEngineering...............Anthony -
Statistics: Challenges and Opportunities for the Twenty-First Century
This is page i Printer: Opaque this Statistics: Challenges and Opportunities for the Twenty-First Century Edited by: Jon Kettenring, Bruce Lindsay, & David Siegmund Draft: 6 April 2003 ii This is page iii Printer: Opaque this Contents 1 Introduction 1 1.1 The workshop . 1 1.2 What is statistics? . 2 1.3 The statistical community . 3 1.4 Resources . 5 2 Historical Overview 7 3 Current Status 9 3.1 General overview . 9 3.1.1 The quality of the profession . 9 3.1.2 The size of the profession . 10 3.1.3 The Odom Report: Issues in mathematics and statistics 11 4 The Core of Statistics 15 4.1 Understanding core interactivity . 15 4.2 A detailed example of interplay . 18 4.3 A set of research challenges . 20 4.3.1 Scales of data . 20 4.3.2 Data reduction and compression. 21 4.3.3 Machine learning and neural networks . 21 4.3.4 Multivariate analysis for large p, small n. 21 4.3.5 Bayes and biased estimation . 22 4.3.6 Middle ground between proof and computational ex- periment. 22 4.4 Opportunities and needs for the core . 23 4.4.1 Adapting to data analysis outside the core . 23 4.4.2 Fragmentation of core research . 23 4.4.3 Growth in the professional needs. 24 4.4.4 Research funding . 24 4.4.5 A Possible Program . 24 5 Statistics in Science and Industry 27 5.1 Biological Sciences . 27 5.2 Engineering and Industry . 33 5.3 Geophysical and Environmental Sciences . -
Why Distinguish Between Statistics and Mathematical Statistics – the Case of Swedish Academia
Why distinguish between statistics and mathematical statistics { the case of Swedish academia Peter Guttorp1 and Georg Lindgren2 1Department of Statistics, University of Washington, Seattle 2Mathematical Statistics, Lund University, Lund 2017/10/11 Abstract A separation between the academic subjects statistics and mathematical statistics has existed in Sweden almost as long as there have been statistics professors. The same distinction has not been maintained in other countries. Why has it been kept so for long in Sweden, and what consequences may it have had? In May 2015 it was 100 years since Mathematical Statistics was formally estab- lished as an academic discipline at a Swedish university where Statistics had existed since the turn of the century. We give an account of the debate in Lund and elsewhere about this division dur- ing the first decades after 1900 and present two of its leading personalities. The Lund University astronomer (and mathematical statistician) C.V.L. Charlier was a lead- ing proponent for a position in mathematical statistics at the university. Charlier's adversary in the debate was Pontus Fahlbeck, professor in political science and statis- tics, who reserved the word statistics for \statistics as a social science". Charlier not only secured the first academic position in Sweden in mathematical statistics for his former Ph.D. student Sven Wicksell, but he also demonstrated that a mathematical statistician can be influential in matters of state, finance, as well as in different nat- ural sciences. Fahlbeck saw mathematical statistics as a set of tools that sometimes could be useful in his brand of statistics. After a summary of the organisational, educational, and scientific growth of the statistical sciences in Sweden that has taken place during the last 50 years, we discuss what effects the Charlier-Fahlbeck divergence might have had on this development.