DOCSLIB.ORG

Testing of Object-Oriented Programming Systems (OOPS): a Fault-Based Approach

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Testing of Object-Oriented Programming Systems (OOPS): a Fault-Based Approach Testing of Object-Oriented Programming Systems (OOPS): A Fault-Based Approach Jane Huffman Hayes Science Applications International Corporation 1213 Jefferson-Davis Highway, Suite 1300 Arlington, Virginia 22202 Abstract. The goal of this paper is to examine the testing of object-oriented systems and to compare and contrast it with the testing of conventional programming language systems, with emphasis on fault-based testing. Conventional system testing, object-oriented system testing, and the application of conventional testing methods to object-oriented software will be examined, followed by a look at the differences between testing of conventional (procedural) software and the testing of object- oriented software. An examination of software faults (defects) will follow, with emphasis on developing a preliminary taxonomy of faults specific to object-oriented systems. Test strategy adequacy will be briefly presented. As a result of these examinations, a set of candidate testing methods for object-oriented programming systems will be identified. 1 Introduction and Overview Two major forces are driving more and more people toward Object-Oriented Programming Systems (OOPS): the need to increase programmer productivity, and the need for higher reliability of the developed systems. It can be expected that sometime in the near future there will be reusable "trusted" object libraries that will require the highest level of integrity. All this points to the need for a means of assuring the quality of OOPS. Specifically, a means for performing verification and validation (V&V) on OOPS is needed. The goal of this paper is to examine the testing of object- oriented systems and to compare and contrast it with the testing of conventional programming language systems, with emphasis on fault-based testing. 1.1 Introduction to Object-Oriented Programming Systems (OOPS) Object-Oriented Programming Systems (OOPS) are characterized by several traits, with the most important one being that information is localized around objects (as opposed to functions or data) [3]. Meyer defines object-oriented design as "the construction of software systems as structured collections of abstract data type implementations" [15]. To understand OOPS, several high level concepts must be introduced: objects, classes, inheritance, polymorphism, and dynamic binding. Objects represent real-world entities and encapsulate both the data and the functions (i.e., behavior and state) which deal with the entity. Objects are run-time instances of classes. Classes define a set of possible objects. A class is meant to implement a user-defined type (ideally an Abstract Data Type (ADT) to support data abstraction). The goal is to keep the implementation details private to the class (information hiding). Inheritance refers to the concept of a new class being declared as an extension or restriction of a previously defined class [15]. Polymorphism refers to the ability to take more than one form. In object-oriented systems, it refers to a reference that can, over time, refer to instances of more than one class. The static type of the reference is determined from the declaration of the entity in the program text. The dynamic type of a polymorphic reference may change from instant to instant during the program execution. Dynamic binding refers to the fact that the code associated with a given procedure call is not known until the moment of the call at run-time [12]. Applying the principles of polymorphism and inheritance, it can be envisioned that a function call could be associated with a polymorphic reference. Thus the function call would need to know the dynamic type of the reference. This provides a tremendous advantage to programmers over conventional programming languages (often referred to as procedural languages): the ability to request an operation without explicitly selecting one of its variants (this choice occurs at run-time) [16]. 1.2 Introduction to Verification and Validation (V&V) Verification and validation refers to two different processes that are used to ensure that computer software reliably performs the functions that it was designed to fulfill. Though different definitions of the terms verification and validation exist, the following definitions from ANSI/IEEE Standard 729-1983 [10] are the most widely accepted and will be used in this paper: Verification is the process of determining whether or not the products of a given phase of the software development cycle fulfill the requirements established during the previous phase. Validation is the process of evaluating software at the end of the software development process to ensure compliance with software requirements. 1.3 Introduction to Testing Testing is a subset of V&V. The IEEE definition of testing is "the process of exercising or evaluating a system or system component by manual or automated means to verify that it satisfies specified requirements or to identify differences between expected and actual results." [10]. Conventional software testing is divided into two categories: static testing and dynamic testing. Static testing analyzes a program without executing it while dynamic testing relies on execution of the program to analyze it [5]. The focus of this paper will be dynamic testing. Dynamic testing is often broken into two categories: black-box testing and white-box testing. Black-box testing refers to functional testing which does not take advantage of implementation details and which examines the program's functional properties. White-box testing, on the other hand, uses information about the program's internal control flow structure or data dependencies. 1.4 Research Approach Figure 1.4-1 depicts the research approach to be taken in this paper. The examination of conventional system testing, object-oriented system testing, and the application of conventional testing methods to object-oriented software will be the first area of concern (sections 2.1 and 2.2). The differences between testing of conventional (procedural) software and the testing of object- oriented software will be examined next (section 2.3). An examination of software faults (defects) will follow, with emphasis on developing a preliminary taxonomy of faults specific to object- oriented systems (section 3). Test strategy adequacy will briefly be presented (section 4). As a result of these examinations, a set of candidate testing methods for object-oriented programming systems will be identified (section 5). It is also possible that some object-oriented software faults which are not currently detected by any testing methods (conventional or object-oriented specific) will be uncovered. It should be noted that despite the future research that is needed, a preliminary recommendation on test methods can be made based on the similarities between OOPS and procedural language systems. 2 Testing Methods An examination of testing methods for conventional programming language systems follows as well as a look at the applicability of these testing methods to object-oriented programming systems. A discussion of testing methods specific to OOPS will then be presented. 2.1 Conventional Programming Language System Testing Methods 2.1.1 Testing Methods . Much has been written concerning the testing of conventional (or procedural) language systems. Some of the earlier works include The Art of Software Testing by Myers [22], "Functional Program Testing" by Howden [9], and more recently Software Testing Techniques by Boris Beizer [2]. The first reference [22] focused on explaining testing and leading the reader through realistic examples. It also discussed numerous testing methods and defined testing terminology. Howden's reference focused on functional testing, which is probably the most frequently applied testing method. Finally, Beizer's text provided a veritable encyclopedia of information on the many conventional testing techniques available and in use. For this paper, the test method taxonomy of Miller is used [19]. Testing is broken into several categories: general testing; special input testing; functional testing; realistic testing; stress testing; performance testing; execution testing; competency testing; active interface testing; structural testing; and error-introduction testing. General testing refers to generic and statistical methods for exercising the program. These methods include: unit/module testing [24]; system testing [7]; regression testing [23]; and ad-hoc testing [7]. Special input testing refers to methods for generating test cases to explore the domain of possible system inputs. Specific testing methods included in this category are: random testing [1]; and domain testing (which includes equivalence partitioning, boundary-value testing, and the category-partition method) [2]. Functional testing refers to methods for selecting test cases to assess the required functionality of a program. Testing methods in the functional testing category include: specific functional requirement testing [9]; and model-based testing [6]. Realistic test methods choose inputs/environments comparable to the intended installation situation. Specific methods include: field testing [28]; and scenario testing [24]. Stress testing refers to choosing inputs/environments which stress the design/implementation of the code. Testing methods in this category include: stability analysis [7]; robustness testing [18]; and limit/range testing [24]. Performance testing refers to measuring various performance aspects with realistic inputs. Specific methods
Load more

Recommended publications
  • January 1993 Reason Toexpect That to Theresearchcommunity
    COMPUTING R ESEARCH N EWS The News Journal of the Computing Research Association January 1993 Vol. 5/No. 1 Membership of Congress changes significantly BY Fred W. Weingarten also was re-elected. He has proven to be attention on high-technology, the seniority for chair of that subcommit- CRA Staff an effective and well-informed chair, committee possibly will attract more tee. His attitude toward science and Although incumbents fared better in but given the turnover in the House members. But it will never have the technology is not well-known. and his rising political star, he may not attraction or political power of the the November elections than was Senate expected, the membership of Congress remain active in R&D policy. Boucher Energy and Commerce Committee, the has changed significantly. Congress has also served on the Energy and Com- Ways and Means Committee or the The Senate is stable because there 118 new members, and some key merce Subcommittee on Telecommuni- Appropriations Committee, which also was less turn-over and science is under members were defeated or retired, so cations and Finance, where he ex- will have openings. Unless they have the Commerce Committee, which is a there will be quite a bit of change in the pressed a great deal of interest in specific interests and expertise in plum. Vice President-elect Al Gore will membership of committees and stimulating the creation of a broadband, science and technology, members with be replaced as chair of the science subcommittees concerned with digital national information infrastruc- seniority and influence tend to gravitate subcommittee.
    [Show full text]
  • Testing for Trustworthiness in Scientific Software
    Testing for Trustworthiness in Scientific Software Daniel Hook Diane Kelly Queen's University Royal Military College of Canada Kingston Ontario Kingston Ontario Abstract expected – and correct – results. Weyuker notes that in Two factors contribute to the difficulty of testing many cases, an oracle is pragmatically unattainable. scientific software. One is the lack of testing oracles – With scientific software, this is almost always the case. a means of comparing software output to expected and Weyuker further comments that if the oracle is correct results. The second is the large number of tests unattainable, then “from the view of correctness testing required when following any standard testing … there is nothing to be gained by performing the technique described in the software engineering test.” For scientific software, this is a depressing and literature. Due to the lack of oracles, scientists use disturbing conclusion. However, there is instead a goal judgment based on experience to assess of trustworthiness rather than correctness that can be trustworthiness, rather than correctness, of their addressed by testing. software. This is an approach well established for In this paper, we first outline a typical development assessing scientific models. However, the problem of environment for scientific software, then briefly assessing software is more complex, exacerbated by discuss testing. This leads to a discussion of the problem of code faults. This highlights the need for correctness and trustworthiness from the view of the effective and efficient testing for code faults in scientist who develops and uses scientific software. We scientific software. Our current research suggests that then introduce new results from our analysis of a small number of well chosen tests may reveal a high samples of scientific software using mutation testing.
    [Show full text]
  • Research Booklet 2020-21
    RESEARCH BOOKLET 2020-21 TABLE OF CONTENTS CONTACT INFORMATION ............................................................................................................................................... III OVERVIEW OF RESEARCH IN COMPUTER SCIENCE AT UCF .................................................................................. V ................................................................................................................................................................................................ 0 FACULTY RESEARCH SUMMARIES ............................................................................................................................. 0 Ulas Bagci ........................................................................................................................................................................ 1 Ladislau Bölöni ................................................................................................................................................................ 1 Mainak Chatterjee ............................................................................................................................................................ 2 Guoxing Chen ................................................................................................................................................................... 2 Carolina Cruz-Neira ..........................................................................................................................................................
    [Show full text]
  • Pdf: Software Testing
    Software Testing Gregory M. Kapfhammer Department of Computer Science Allegheny College [email protected] I shall not deny that the construction of these testing programs has been a major intellectual effort: to convince oneself that one has not overlooked “a relevant state” and to convince oneself that the testing programs generate them all is no simple matter. The encouraging thing is that (as far as we know!) it could be done. Edsger W. Dijkstra [Dijkstra, 1968] 1 Introduction When a program is implemented to provide a concrete representation of an algorithm, the developers of this program are naturally concerned with the correctness and performance of the implementation. Soft- ware engineers must ensure that their software systems achieve an appropriate level of quality. Software verification is the process of ensuring that a program meets its intended specification [Kaner et al., 1993]. One technique that can assist during the specification, design, and implementation of a software system is software verification through correctness proof. Software testing, or the process of assessing the func- tionality and correctness of a program through execution or analysis, is another alternative for verifying a software system. As noted by Bowen, Hinchley, and Geller, software testing can be appropriately used in conjunction with correctness proofs and other types of formal approaches in order to develop high quality software systems [Bowen and Hinchley, 1995, Geller, 1978]. Yet, it is also possible to use software testing techniques in isolation from program correctness proofs or other formal methods. Software testing is not a “silver bullet” that can guarantee the production of high quality software systems.
    [Show full text]
  • Experience Doing Software Fault Prediction in an Industrial Environment)
    UCF Dept. of Computer Science and the College of Engineering and Computer Science I Know Where You're Hiding! (Experience Doing Software Fault Prediction in an Industrial Environment) When validating a software system, it would obviously be very valuable to know in advance which files in the next release ofa large software system are most likely to contain the largest numbers of faults. To accomplish this, we developed negative binomial regression models and used them to predict the expected number of faults in each file of the next release of large industrial software systems. The predictions are based on code char- acteristics and fault and modification history data. This talk will discuss what we have learned from applying the modelsev to eral large industrial systems, each with multiple years of field exposure. I will also discuss our success in making accurate predictions and some of the issues that had to be considered. University Distinguished Professor, College of Engineering and Computer Science, University of Central Florida Elaine Weyuker is a University Distinguished Professor, College of Engineering and Computer Science, University of Central Florida. Before joining UCF, Elaine was a Fellow and Distinguished Member of the Technical Staff at AT&T Labs and Bell Labs, a Professor of Computer Science at the Courant Institute of Mathematical Sciences of New York Universi- ty, a Lecturer at the City University of New York, a Systems Engineer at IBM, and a programmer at Texaco, as well as having served as a consultant for several large international companies. Her research expertise includes techniques and tools to improve the quality of software systems through systematic validation activities, including the development of testing, assessment and software faultprediction models.
    [Show full text]
  • Coverage Testing in a Production Software Development Environment Kent Bortz Regis University
    Regis University ePublications at Regis University All Regis University Theses Fall 2006 Coverage Testing in a Production Software Development Environment Kent Bortz Regis University Follow this and additional works at: https://epublications.regis.edu/theses Part of the Computer Sciences Commons Recommended Citation Bortz, Kent, "Coverage Testing in a Production Software Development Environment" (2006). All Regis University Theses. 416. https://epublications.regis.edu/theses/416 This Thesis - Open Access is brought to you for free and open access by ePublications at Regis University. It has been accepted for inclusion in All Regis University Theses by an authorized administrator of ePublications at Regis University. For more information, please contact [email protected]. Regis University School for Professional Studies Graduate Programs Final Project/Thesis Disclaimer Use of the materials available in the Regis University Thesis Collection (“Collection”) is limited and restricted to those users who agree to comply with the following terms of use. Regis University reserves the right to deny access to the Collection to any person who violates these terms of use or who seeks to or does alter, avoid or supersede the functional conditions, restrictions and limitations of the Collection. The site may be used only for lawful purposes. The user is solely responsible for knowing and adhering to any and all applicable laws, rules, and regulations relating or pertaining to use of the Collection. All content in this Collection is owned by and subject to the exclusive control of Regis University and the authors of the materials. It is available only for research purposes and may not be used in violation of copyright laws or for unlawful purposes.
    [Show full text]
  • Database Test Data Generation
    Test Data Generation for Relational Database Applications David Chays Department of Computer and Information Science Technical Report TR-CIS-2005-01 01/12/2005 TEST DATA GENERATION FOR RELATIONAL DATABASE APPLICATIONS DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY (Computer & Information Science) at the POLYTECHNIC UNIVERSITY by D. Chays January 2004 Approved : Department Head Copy No. Approved by the Guidance Committee : Major : Computer & Information Science Phyllis Frankl Professor of Computer & Information Science Gleb Naumovich Assistant Professor of Computer & Information Science Filippos Vokolos Assistant Professor of Computer & Information Science Minor : Electrical Engineering Shivendra Panwar Professor of Electrical Engineering Microfilm or other copies of this dissertation are obtainable from UMI Dissertations Publishing Bell & Howell Information and Learning 300 North Zeeb Road P.O. Box 1346 Ann Arbor, Michigan 48106-1346 iv VITA David Chays was born in Brooklyn, New York in November 1972. He received his B.S. degree in Computer Science from Brooklyn College of the City University of New York in 1995 and his M.S. degree in Computer Science from Polytechnic University in Brooklyn, New York, in 1998. After pass- ing the Ph.D. Qualifying Exam at Polytechnic University in 1999, he began working on the database application testing project leading to his thesis, under the supervision of Phyllis Frankl. His research interests are in the areas of software testing, database systems, and computer security. The work presented in this thesis was supported by teaching fellowships from the department of Computer and Information Science at Polytechnic University and grants from the National Science Foundation and the Department of Education.
    [Show full text]
  • Empirical Software Engineering at Microsoft Research
    Software Analytics Harlan D. Mills Award Acceptance Speech Nachi Nagappan © Microsoft Corporation About Me • My name is Nachiappan. I also go by Nachi. • https://nachinagappan.github.io/ • Graduated with a PhD with Laurie Williams. • I read a lot of Franco-Belgian comics (Bande dessinées) • Attend Comic conventions • Miniature railroad modeling (HO and G). © Microsoft Corporation 3 © Microsoft Corporation 4 © Microsoft Corporation Courtney Miller Jenna Butler Danielle Gonzalez Rangeet Pan Yu Huang Jazette Johnson Paige Rodeghero Rini Chattopadhyay 5 © Microsoft Corporation Courtney Miller Jenna Butler Danielle Gonzalez Rangeet Pan Yu Huang Jazette Johnson Paige Rodeghero Rini Chattopadhyay 6 © Microsoft Corporation What metrics are the If I increase test coverage, will that best predictors of failures? actually increase software quality? What is the data quality level Are there any metrics that are indicators of used in empirical studies and failures in both Open Source and Commercial how much does it actually domains? matter? I just submitted a bug report. Will it be fixed? Should I be writing unit tests in my software How can I tell if a piece of software will have vulnerabilities? project? Is strong code ownership good or Do cross-cutting concerns bad for software quality? cause defects? Does Distributed/Global software Does Test Driven Development (TDD) development affect quality? produce better code in shorter time? 7 © Microsoft Corporation History of Software Analytics 1976: Thomas McCabe code complexity 1971: Fumio Akiyama 1981:
    [Show full text]
  • Software Systems Engineering Programmes a Capability Approach
    The Journal of Systems and Software 125 (2017) 354–364 Contents lists available at ScienceDirect The Journal of Systems and Software journal homepage: www.elsevier.com/locate/jss R Software Systems Engineering programmes a capability approach ∗ Carl Landwehr a, Jochen Ludewig b, Robert Meersman c, David Lorge Parnas d, , Peretz Shoval e, Yair Wand f, David Weiss g, Elaine Weyuker h a Cyber Security Policy and Research Institute, George Washington University, Washington, DC, USA b Institut für Software, Universität Stuttgart, Stuttgart, Germany c Institut für Informationssysteme und Computer Medien (IICM), Fakultät für Informatik, TU Graz, Graz, Austria d Middle Road Software, Ottawa, Ontario, Canada e Ben-Gurion University, Be’er-Sheva, Israel f Sauder School of Business, University of British Columbia, Vancouver, BC, Canada g Iowa State University, Ames Iowa, USA h Mälardalen University, Västerås, Sweden and University of Central Florida, Orlando, FL USA a r t i c l e i n f o a b s t r a c t Article history: This paper discusses third-level educational programmes that are intended to prepare their graduates for Received 24 May 2016 a career building systems in which software plays a major role. Such programmes are modelled on tradi- Revised 22 November 2016 tional Engineering programmes but have been tailored to applications that depend heavily on software. Accepted 19 December 2016 Rather than describe knowledge that should be taught, we describe capabilities that students should Available online 23 December 2016 acquire in these programmes. The paper begins with some historical observations about the software Keywords: development field. Engineering ©2016 Elsevier Inc.
    [Show full text]
  • Interim Report of a Review of the Next Generation Air Transportation System Enterprise Architecture, Software, Safety, and Human Factors
    This PDF is available from The National Academies Press at http://www.nap.edu/catalog.php?record_id=18618 Interim Report of a Review of the Next Generation Air Transportation System Enterprise Architecture, Software, Safety, and Human Factors ISBN Committee to Review the Enterprise Architecture, Software Development 978-0-309-29831-5 Approach, and Safety and Human Factor Design of the Next Generation Air Transportation System; Computer Science and Telecommunications 40 pages Board; Division on Engineering and Physical Sciences; National Research 8.5 x 11 2014 Council Visit the National Academies Press online and register for... Instant access to free PDF downloads of titles from the NATIONAL ACADEMY OF SCIENCES NATIONAL ACADEMY OF ENGINEERING INSTITUTE OF MEDICINE NATIONAL RESEARCH COUNCIL 10% off print titles Custom notification of new releases in your field of interest Special offers and discounts Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the National Academies Press. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Request reprint permission for this book Copyright © National Academy of Sciences. All rights reserved. Interim Report of a Review of the Next Generation Air Transportation System Enterprise Architecture, Software, Safety, and Human Factors Interim Report of a Review of the Next Generation Air Transportation System Enterprise Architecture, Software, Safety, and Human Factors Committee to Review the Enterprise Architecture, Software Development Approach, and Safety and Human Factor Design of the Next Generation Air Transportation System Computer Science and Telecommunications Board Division on Engineering and Physical Sciences Copyright © National Academy of Sciences.
    [Show full text]
  • Shenkar College Report 2013.Pages
    ! ! ! ! ! ! ! ! ! Committee for the Evaluation of Software Engineering and Information Systems Engineering Study Programmes! ! ! ! Shenkar College ! Department of Software Engineering ! Evaluation Report! ! ! ! ! ! ! ! ! ! ! ! 13.04.14 !1/!33 ! ! ! Contents! ! Chapter 1: Background 3 Chapter 2: Committee Procedures 4 Chapter 3: Executive Summary 5 Chapter 4: Evaluation Criteria for System Software Engineering Programmes 7 Chapter 5: Evaluation of Software Engineering Study Programme at Shenkar College 27 Chapter 6: Summary of Recommendations and Timetable 33 Appendices Appendix 1 – Letter of Appointment !Appendix 2 - Schedule of the visit 13.04.14 !2/!33 Chapter 1: Background The Council for Higher Education (CHE) decided to evaluate the study programmes in Software Engineering and Information Systems Engineering during the 2013 academic year. Following the decision of the CHE, the Minister of Education, who serves ex officio as Chairperson of the CHE, appointed a review committee consisting of: • Prof. David Parnas (Emeritus) – Engineering, McMaster University, Canada - Committee chair • Prof. Carl Landwehr - Cyber Security Policy and Research Institute, George Washington University, USA • Prof. Jochen Ludewig - Chair of Software Engineering, Stuttgart University, Germany • Prof. Robert Meersman, Department of Computer Science, The Vrije University - Brussels, Belgium • Prof. Peretz Shoval – Department of Information Systems Engineering, Ben Gurion University, Israel • Prof. Yair Wand1 - Sauder School of Business, The University of British
    [Show full text]
  • A Specification-Based Coverage Metric to Evaluate Test Sets
    NISTIR 6403 A Specification-Based Coverage Metric to Evaluate Test Sets Paul E. Ammann George Mason University Information & Software Engineering Dept. Fairfax, VA 22033 Paul E. Black U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology 100 Bureau Drive Gaithersburg, MD 20899 (£ 100 NIST A Specification-Based Coverage Metric to Evaluate Test Sets Paul E. Ammann George Mason University Information & Software Engineering Dept. Fairfax, VA 22033 Paul E. Black U.S. DEPARTMENT OF COMMERCE Technology Administration National Institute of Standards and Technology 100 Bureau Drive Gaithersburg, MD 20899 October 1999 /iT OF U.S. DEPARTMENT OF COMMERCE William M. Daley, Secretary TECHNOLOGY ADMINISTRATION Gary R. Bachula, Acting Under Secretary for Technology NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY Raymond G. Hammer, Director A Specification-Based Coverage Metric to Evaluate Test Sets Paul E. Ammann* Paul E. Black George Mason University NIST pammann@gmu. edu paul. [email protected] Abstract Software developers use a variety of methods, including both formal methods and testing, to argue that their systems are suitable for building high as- surance applications. In this paper, we develop another connection between formal methods and testing by defining a specification-based coverage metric to evaluate test sets. Formal methods in the form of a model checker supply the necessary automation to make the metric practical. The metric gives the software developer assurance that a given test set is sufficiently sensitive to the structure of an application’s specification. In this paper, we develop the necessary foundation for the metric and then illustrate the metric on an example.
    [Show full text]