Study of White Box, Black Box and Grey Box Testing Techniques
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ON SOFTWARE TESTING and SUBSUMING MUTANTS an Empirical Study
ON SOFTWARE TESTING AND SUBSUMING MUTANTS An empirical study Master Degree Project in Computer Science Advanced level 15 credits Spring term 2014 András Márki Supervisor: Birgitta Lindström Examiner: Gunnar Mathiason Course: DV736A WECOA13: Web Computing Magister Summary Mutation testing is a powerful, but resource intense technique for asserting software quality. This report investigates two claims about one of the mutation operators on procedural logic, the relation operator replacement (ROR). The constrained ROR mutant operator is a type of constrained mutation, which targets to lower the number of mutants as a “do smarter” approach, making mutation testing more suitable for industrial use. The findings in the report shows that the hypothesis on subsumption is rejected if mutants are to be detected on function return values. The second hypothesis stating that a test case can only detect a single top-level mutant in a subsumption graph is also rejected. The report presents a comprehensive overview on the domain of mutation testing, displays examples of the masking behaviour previously not described in the field of mutation testing, and discusses the importance of the granularity where the mutants should be detected under execution. The contribution is based on literature survey and experiment. The empirical findings as well as the implications are discussed in this master dissertation. Keywords: Software Testing, Mutation Testing, Mutant Subsumption, Relation Operator Replacement, ROR, Empirical Study, Strong Mutation, Weak Mutation -
Types of Software Testing
Types of Software Testing We would be glad to have feedback from you. Drop us a line, whether it is a comment, a question, a work proposition or just a hello. You can use either the form below or the contact details on the rightt. Contact details [email protected] +91 811 386 5000 1 Software testing is the way of assessing a software product to distinguish contrasts between given information and expected result. Additionally, to evaluate the characteristic of a product. The testing process evaluates the quality of the software. You know what testing does. No need to explain further. But, are you aware of types of testing. It’s indeed a sea. But before we get to the types, let’s have a look at the standards that needs to be maintained. Standards of Testing The entire test should meet the user prerequisites. Exhaustive testing isn’t conceivable. As we require the ideal quantity of testing in view of the risk evaluation of the application. The entire test to be directed ought to be arranged before executing it. It follows 80/20 rule which expresses that 80% of defects originates from 20% of program parts. Start testing with little parts and extend it to broad components. Software testers know about the different sorts of Software Testing. In this article, we have incorporated majorly all types of software testing which testers, developers, and QA reams more often use in their everyday testing life. Let’s understand them!!! Black box Testing The black box testing is a category of strategy that disregards the interior component of the framework and spotlights on the output created against any input and performance of the system. -
Thesis Template
Automated Testing: Requirements Propagation via Model Transformation in Embedded Software Nader Kesserwan A Thesis In The Concordia Institute For Information System Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy (Information and Systems Engineering) at Concordia University Montreal, Quebec, Canada March 2020 © Nader Kesserwan 2020 ABSTRACT Automated Testing: Requirements Propagation via Model Transformation in Embedded Software Nader Kesserwan, Ph.D. Concordia University, 2020 Testing is the most common activity to validate software systems and plays a key role in the software development process. In general, the software testing phase takes around 40-70% of the effort, time and cost. This area has been well researched over a long period of time. Unfortunately, while many researchers have found methods of reducing time and cost during the testing process, there are still a number of important related issues such as generating test cases from UCM scenarios and validate them need to be researched. As a result, ensuring that an embedded software behaves correctly is non-trivial, especially when testing with limited resources and seeking compliance with safety-critical software standard. It thus becomes imperative to adopt an approach or methodology based on tools and best engineering practices to improve the testing process. This research addresses the problem of testing embedded software with limited resources by the following. First, a reverse-engineering technique is exercised on legacy software tests aims to discover feasible transformation from test layer to test requirement layer. The feasibility of transforming the legacy test cases into an abstract model is shown, along with a forward engineering process to regenerate the test cases in selected test language. -
Software Testing: Essential Phase of SDLC and a Comparative Study Of
International Journal of System and Software Engineering Volume 5 Issue 2, December 2017 ISSN.: 2321-6107 Software Testing: Essential Phase of SDLC and a Comparative Study of Software Testing Techniques Sushma Malik Assistant Professor, Institute of Innovation in Technology and Management, Janak Puri, New Delhi, India. Email: [email protected] Abstract: Software Development Life-Cycle (SDLC) follows In the software development process, the problem (Software) the different activities that are used in the development of a can be dividing in the following activities [3]: software product. SDLC is also called the software process ∑ Understanding the problem and it is the lifeline of any Software Development Model. ∑ Decide a plan for the solution Software Processes decide the survival of a particular software development model in the market as well as in ∑ Coding for the designed solution software organization and Software testing is a process of ∑ Testing the definite program finding software bugs while executing a program so that we get the zero defect software. The main objective of software These activities may be very complex for large systems. So, testing is to evaluating the competence and usability of a each of the activity has to be broken into smaller sub-activities software. Software testing is an important part of the SDLC or steps. These steps are then handled effectively to produce a because through software testing getting the quality of the software project or system. The basic steps involved in software software. Lots of advancements have been done through project development are: various verification techniques, but still we need software to 1) Requirement Analysis and Specification: The goal of be fully tested before handed to the customer. -
Testing, Debugging & Verification
Testing, debugging & verification Srinivas Pinisetty This course Introduction to techniques to get (some) certainty that your program does what it’s supposed to. Specification: An unambiguous description of what a function (program) should do. Bug: failure to meet specification. What is a Bug? Basic Terminology ● Defect (aka bug, fault) introduced into code by programmer (not always programmer's fault, if, e.g., requirements changed) ● Defect may cause infection of program state during execution (not all defects cause infection) ● Infected state propagates during execution (infected parts of states may be overwritten or corrected) ● Infection may cause a failure: an externally observable error (including, e.g., non-termination) Terminology ● Testing - Check for bugs ● Debugging – Relating a failure to a defect (systematically find source of failure) ● Specification - Describe what is a bug ● (Formal) Verification - Prove that there are no bugs Cost of certainty Formal Verification Property based testing Unit testing Man hours (*) Graph not based on data, only indication More certainty = more work Contract metaphor Supplier: (callee) Implementer of method Client: (caller) Implementer of calling method or user Contract: Requires (precondition): What the client must ensure Ensures (postcondition): What the supplier must ensure ● Testing ● Formal specification ○ Unit testing ○ Logic ■ Coverage criteria ■ Propositional logic ● Control-Flow based ■ Predicate Logic ● Logic Based ■ SAT ■ Extreme Testing ■ SMT ■ Mutation testing ○ Dafny ○ Input -
Note 5. Testing
Computer Science and Software Engineering University of Wisconsin - Platteville Note 5. Testing Yan Shi Lecture Notes for SE 3730 / CS 5730 Outline . Formal and Informal Reviews . Levels of Testing — Unit, Structural Coverage Analysis Input Coverage Testing: • Equivalence class testing • Boundary value analysis testing CRUD testing All pairs — integration, — system, — acceptance . Regression Testing Static and Dynamic Testing . Static testing: the software is not actually executed. — Generally not detailed testing — Reviews, inspections, walkthrough . Dynamic testing: test the dynamic behavior of the software — Usually need to run the software. Black, White and Grey Box Testing . Black box testing: assume no knowledge about the code, structure or implementation. White box testing: fully based on knowledge of the code, structure or implementation. Grey box testing: test with only partial knowledge of implementation. — E.g., algorithm review. Reviews . Static analysis and dynamic analysis . Black-box testing and white-box testing . Reviews are static white-box (?) testing. — Formal design reviews: DR / FDR — Peer reviews: inspections and walkthrough Formal Design Review . The only reviews that are necessary for approval of the design product. The development team cannot continue to the next stage without this approval. Maybe conducted at any development milestone: — Requirement, system design, unit/detailed design, test plan, code, support manual, product release, installation plan, etc. FDR Procedure . Preparation: — find review members (5-10), — review in advance: could use the help of checklist. A short presentation of the document. Comments by the review team. Verification and validation based on comments. Decision: — Full approval: immediate continuation to the next phase. — Partial approval: immediate continuation for some part, major action items for the remainder. -
Breeding Software Test Cases for Pairwise Testing Using GA GJCST Classification Dr
Global Journal of Computer Science and Technology Vol. 10 Issue 4 Ver. 1.0 June 2010 P a g e | 97 Breeding Software Test Cases for Pairwise Testing Using GA GJCST Classification Dr. Rakesh Kumar1 Surjeet Singh2 D.2.5, D.2.12 Abstract- All-pairs testing or pairwise testing is a specification- has improved with testing, it is still not clear to the based combinatorial testing method, which requires that for customers that they are receiving the return of the each pair of input parameters to a system (typically, a software investment they demand. Software testing is an important algorithm), each combination of valid values of these two activity of the software development process. It is a critical parameters be covered by at least one test case [TAI02]. element of software quality assurance. A set of possible Pairwise testing has become an indispensable tool in a software tester’s toolbox. This paper pays special attention to usability of inputs for any software system can be too large to be tested the pairwise testing technique. In this paper, we propose a new exhaustively. Techniques like equivalence class partitioning test generation strategy for pairwise testing using Genetic and boundary-value analysis help convert even a large Algorithm (GA). We compare the result with the random number of test levels into a much smaller set with testing and pairwise testing strategy and find that applying GA comparable defect-detection capability. If software under for pairwise testing performs better result. Information on at test can be influenced by a number of such aspects, least 20 tools that can generate pairwise test cases, have so far exhaustive testing again becomes impracticable. -
Different Types of Testing
Different Types of Testing Performance testing a. Performance testing is designed to test run time performance of software within the context of an integrated system. It is not until all systems elements are fully integrated and certified as free of defects the true performance of a system can be ascertained b. Performance tests are often coupled with stress testing and often require both hardware and software infrastructure. That is, it is necessary to measure resource utilization in an exacting fashion. External instrumentation can monitor intervals, log events. By instrument the system, the tester can uncover situations that lead to degradations and possible system failure Security testing If your site requires firewalls, encryption, user authentication, financial transactions, or access to databases with sensitive data, you may need to test these and also test your site's overall protection against unauthorized internal or external access Exploratory Testing Often taken to mean a creative, internal software test that is not based on formal test plans or test cases; testers may be learning the software as they test it Benefits Realization tests With the increased focus on the value of Business returns obtained from investments in information technology, this type of test or analysis is becoming more critical. The benefits realization test is a test or analysis conducted after an application is moved into production in order to determine whether the application is likely to deliver the original projected benefits. The analysis is usually conducted by the business user or client group who requested the project and results are reported back to executive management Mutation Testing Mutation testing is a method for determining if a set of test data or test cases is useful, by deliberately introducing various code changes ('bugs') and retesting with the original test data/cases to determine if the 'bugs' are detected. -
Guidelines on Minimum Standards for Developer Verification of Software
Guidelines on Minimum Standards for Developer Verification of Software Paul E. Black Barbara Guttman Vadim Okun Software and Systems Division Information Technology Laboratory July 2021 Abstract Executive Order (EO) 14028, Improving the Nation’s Cybersecurity, 12 May 2021, di- rects the National Institute of Standards and Technology (NIST) to recommend minimum standards for software testing within 60 days. This document describes eleven recommen- dations for software verification techniques as well as providing supplemental information about the techniques and references for further information. It recommends the following techniques: • Threat modeling to look for design-level security issues • Automated testing for consistency and to minimize human effort • Static code scanning to look for top bugs • Heuristic tools to look for possible hardcoded secrets • Use of built-in checks and protections • “Black box” test cases • Code-based structural test cases • Historical test cases • Fuzzing • Web app scanners, if applicable • Address included code (libraries, packages, services) The document does not address the totality of software verification, but instead recom- mends techniques that are broadly applicable and form the minimum standards. The document was developed by NIST in consultation with the National Security Agency. Additionally, we received input from numerous outside organizations through papers sub- mitted to a NIST workshop on the Executive Order held in early June, 2021 and discussion at the workshop as well as follow up with several of the submitters. Keywords software assurance; verification; testing; static analysis; fuzzing; code review; software security. Disclaimer Any mention of commercial products or reference to commercial organizations is for infor- mation only; it does not imply recommendation or endorsement by NIST, nor is it intended to imply that the products mentioned are necessarily the best available for the purpose. -
Jia and Harman's an Analysis and Survey of the Development Of
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 1 An Analysis and Survey of the Development of Mutation Testing Yue Jia Student Member, IEEE, and Mark Harman Member, IEEE Abstract— Mutation Testing is a fault–based software testing Besides using Mutation Testing at the software implementation technique that has been widely studied for over three decades. level, it has also been applied at the design level to test the The literature on Mutation Testing has contributed a set of specifications or models of a program. For example, at the design approaches, tools, developments and empirical results. This paper level Mutation Testing has been applied to Finite State Machines provides a comprehensive analysis and survey of Mutation Test- ing. The paper also presents the results of several development [20], [28], [88], [111], State charts [95], [231], [260], Estelle trend analyses. These analyses provide evidence that Mutation Specifications [222], [223], Petri Nets [86], Network protocols Testing techniques and tools are reaching a state of maturity [124], [202], [216], [238], Security Policies [139], [154], [165], and applicability, while the topic of Mutation Testing itself is the [166], [201] and Web Services [140], [142], [143], [193], [245], subject of increasing interest. [259]. Index Terms— mutation testing, survey Mutation Testing has been increasingly and widely studied since it was first proposed in the 1970s. There has been much research work on the various kinds of techniques seeking to I. INTRODUCTION turn Mutation Testing into a practical testing approach. However, Mutation Testing is a fault-based testing technique which pro- there is little survey work in the literature on Mutation Testing. -
What Are We Really Testing in Mutation Testing for Machine
What Are We Really Testing in Mutation Testing for Machine Learning? A Critical Reflection Annibale Panichella Cynthia C. S. Liem [email protected] [email protected] Delft University of Technology Delft University of Technology Delft, The Netherlands Delft, The Netherlands Abstract—Mutation testing is a well-established technique for niques, as extensively surveyed by Ben Braiek & Khom [5], assessing a test suite’s quality by injecting artificial faults into as well as Zhang et al. [6]. While this is a useful development, production code. In recent years, mutation testing has been in this work, we argue that current testing for ML approaches extended to machine learning (ML) systems, and deep learning (DL) in particular; researchers have proposed approaches, tools, are not sufficiently explicit about what is being tested exactly. and statistically sound heuristics to determine whether mutants In particular, in this paper, we focus on mutation testing, a in DL systems are killed or not. However, as we will argue well-established white-box testing technique, which recently in this work, questions can be raised to what extent currently has been applied in the context of DL. As we will show, used mutation testing techniques in DL are actually in line with several fundaments of classical mutation testing are currently the classical interpretation of mutation testing. We observe that ML model development resembles a test-driven development unclearly or illogically mapped to the ML context. After (TDD) process, in which a training algorithm (‘programmer’) illustrating this, we will conclude this paper with several generates a model (program) that fits the data points (test concrete action points for improvement. -
Using Mutation Testing to Measure Behavioural Test Diversity
Using mutation testing to measure behavioural test diversity Francisco Gomes de Oliveira Neto, Felix Dobslaw, Robert Feldt Chalmers and the University of Gothenburg Dept. of Computer Science and Engineering Gothenburg, Sweden [email protected], fdobslaw,[email protected] Abstract—Diversity has been proposed as a key criterion [7], or even over the entire set of tests altogether [8]. In to improve testing effectiveness and efficiency. It can be used other words, the goal is then, to obtain a test suite able to to optimise large test repositories but also to visualise test exercise distinct parts of the SUT to increase the fault detection maintenance issues and raise practitioners’ awareness about waste in test artefacts and processes. Even though these diversity- rate [5], [8]. However, the main focus in diversity-based testing based testing techniques aim to exercise diverse behavior in the has been on calculating diversity on, typically static, artefacts system under test (SUT), the diversity has mainly been measured while there has been little focus on considering the outcomes on and between artefacts (e.g., inputs, outputs or test scripts). of existing test cases, i.e., whether similar tests fail together Here, we introduce a family of measures to capture behavioural when executed against the same SUT. Many studies report that diversity (b-div) of test cases by comparing their executions and failure outcomes. Using failure information to capture the using the outcomes of test cases has proven to be an effective SUT behaviour has been shown to improve effectiveness of criteria to prioritise tests, but there are many challenges with history-based test prioritisation approaches.