3,655 research outputs found
Evaluating and comparing fault-based testing strategies for general Boolean specifications: A series of experiments
A great amount of fault-based testing strategies have been proposed to generate test cases for detecting certain types of faults in Boolean specifications. However, most of the previous studies on these strategies were focused on the Boolean expressions in the disjunctive normal form (DNF), even the irredundant DNF (IDNF)-little work has been conducted to comprehensively investigate their performance on general Boolean specifications. In this study, we conducted a series of experiments to evaluate and compare 18 fault-based testing strategies using over 4000 randomly generated fault-seeded Boolean expressions. In the experiments, a testing strategy is regarded as effective and efficient if it can detect most of the seeded faults using a small number of test cases. Our experimental results show that if a testing strategy is highly effective and efficient when testing the Boolean expressions in the IDNF, it also shows high effectiveness and efficiency on general Boolean expressions. It is found that one family of fault-based testing strategies, namely MUMCUT, normally deliver the best performance among all the 18 strategies. Our study provides an in-depth understanding and insight of fault-based testing for general Boolean expressions
A Historical Perspective on Runtime Assertion Checking in Software Development
This report presents initial results in the area of software testing and analysis produced as part of the Software Engineering Impact Project. The report describes the historical development of runtime assertion checking, including a description of the origins of and significant features associated with assertion checking mechanisms, and initial findings about current industrial use. A future report will provide a more comprehensive assessment of development practice, for which we invite readers of this report to contribute information
Qualitative temporal analysis: Towards a full implementation of the Fault Tree Handbook
The Fault tree handbook has become the de facto standard for fault tree analysis (FTA), defining the notation and mathematical foundation of this widely used safety analysis technique. The Handbook recognises that classical combinatorial fault trees employing only Boolean gates cannot capture the potentially critical significance of the temporal ordering of failure events in a system. Although the Handbook proposes two dynamic gates that could remedy this, a Priority-AND and an Exclusive-OR gate, these gates were never accurately defined. This paper proposes extensions to the logical foundation of fault trees that enable use of these dynamic gates in an extended and more powerful FTA. The benefits of this approach are demonstrated on a generic triple-module standby redundant system exhibiting dynamic behaviour
Automated Fixing of Programs with Contracts
This paper describes AutoFix, an automatic debugging technique that can fix
faults in general-purpose software. To provide high-quality fix suggestions and
to enable automation of the whole debugging process, AutoFix relies on the
presence of simple specification elements in the form of contracts (such as
pre- and postconditions). Using contracts enhances the precision of dynamic
analysis techniques for fault detection and localization, and for validating
fixes. The only required user input to the AutoFix supporting tool is then a
faulty program annotated with contracts; the tool produces a collection of
validated fixes for the fault ranked according to an estimate of their
suitability.
In an extensive experimental evaluation, we applied AutoFix to over 200
faults in four code bases of different maturity and quality (of implementation
and of contracts). AutoFix successfully fixed 42% of the faults, producing, in
the majority of cases, corrections of quality comparable to those competent
programmers would write; the used computational resources were modest, with an
average time per fix below 20 minutes on commodity hardware. These figures
compare favorably to the state of the art in automated program fixing, and
demonstrate that the AutoFix approach is successfully applicable to reduce the
debugging burden in real-world scenarios.Comment: Minor changes after proofreadin
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Comparing test sets and criteria in the presence of test hypotheses and fault domains
A number of authors have considered the problem of comparing test sets and criteria. Ideally
test sets are compared using a preorder with the property that test set T1 is at least as strong
as T2 if whenever T2 determines that an implementation p is faulty, T1 will also determine that
p is faulty. This notion can be extended to test criteria. However, it has been noted that very
few test sets and criteria are comparable under such an ordering; instead orderings are based
on weaker properties such as subsumes. This paper explores an alternative approach, in which
comparisons are made in the presence of a test hypothesis or fault domain. This approach allows
strong statements about fault detecting ability to be made and yet for a number of test sets and
criteria to be comparable. It may also drive incremental test generation
Stateful Testing: Finding More Errors in Code and Contracts
Automated random testing has shown to be an effective approach to finding
faults but still faces a major unsolved issue: how to generate test inputs
diverse enough to find many faults and find them quickly. Stateful testing, the
automated testing technique introduced in this article, generates new test
cases that improve an existing test suite. The generated test cases are
designed to violate the dynamically inferred contracts (invariants)
characterizing the existing test suite. As a consequence, they are in a good
position to detect new errors, and also to improve the accuracy of the inferred
contracts by discovering those that are unsound. Experiments on 13 data
structure classes totalling over 28,000 lines of code demonstrate the
effectiveness of stateful testing in improving over the results of long
sessions of random testing: stateful testing found 68.4% new errors and
improved the accuracy of automatically inferred contracts to over 99%, with
just a 7% time overhead.Comment: 11 pages, 3 figure
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