58,335 research outputs found
Multiversion software reliability through fault-avoidance and fault-tolerance
In this project we have proposed to investigate a number of experimental and theoretical issues associated with the practical use of multi-version software in providing dependable software through fault-avoidance and fault-elimination, as well as run-time tolerance of software faults. In the period reported here we have working on the following: We have continued collection of data on the relationships between software faults and reliability, and the coverage provided by the testing process as measured by different metrics (including data flow metrics). We continued work on software reliability estimation methods based on non-random sampling, and the relationship between software reliability and code coverage provided through testing. We have continued studying back-to-back testing as an efficient mechanism for removal of uncorrelated faults, and common-cause faults of variable span. We have also been studying back-to-back testing as a tool for improvement of the software change process, including regression testing. We continued investigating existing, and worked on formulation of new fault-tolerance models. In particular, we have partly finished evaluation of Consensus Voting in the presence of correlated failures, and are in the process of finishing evaluation of Consensus Recovery Block (CRB) under failure correlation. We find both approaches far superior to commonly employed fixed agreement number voting (usually majority voting). We have also finished a cost analysis of the CRB approach
Test Set Diameter: Quantifying the Diversity of Sets of Test Cases
A common and natural intuition among software testers is that test cases need
to differ if a software system is to be tested properly and its quality
ensured. Consequently, much research has gone into formulating distance
measures for how test cases, their inputs and/or their outputs differ. However,
common to these proposals is that they are data type specific and/or calculate
the diversity only between pairs of test inputs, traces or outputs.
We propose a new metric to measure the diversity of sets of tests: the test
set diameter (TSDm). It extends our earlier, pairwise test diversity metrics
based on recent advances in information theory regarding the calculation of the
normalized compression distance (NCD) for multisets. An advantage is that TSDm
can be applied regardless of data type and on any test-related information, not
only the test inputs. A downside is the increased computational time compared
to competing approaches.
Our experiments on four different systems show that the test set diameter can
help select test sets with higher structural and fault coverage than random
selection even when only applied to test inputs. This can enable early test
design and selection, prior to even having a software system to test, and
complement other types of test automation and analysis. We argue that this
quantification of test set diversity creates a number of opportunities to
better understand software quality and provides practical ways to increase it.Comment: In submissio
Is the Stack Distance Between Test Case and Method Correlated With Test Effectiveness?
Mutation testing is a means to assess the effectiveness of a test suite and
its outcome is considered more meaningful than code coverage metrics. However,
despite several optimizations, mutation testing requires a significant
computational effort and has not been widely adopted in industry. Therefore, we
study in this paper whether test effectiveness can be approximated using a more
light-weight approach. We hypothesize that a test case is more likely to detect
faults in methods that are close to the test case on the call stack than in
methods that the test case accesses indirectly through many other methods.
Based on this hypothesis, we propose the minimal stack distance between test
case and method as a new test measure, which expresses how close any test case
comes to a given method, and study its correlation with test effectiveness. We
conducted an empirical study with 21 open-source projects, which comprise in
total 1.8 million LOC, and show that a correlation exists between stack
distance and test effectiveness. The correlation reaches a strength up to 0.58.
We further show that a classifier using the minimal stack distance along with
additional easily computable measures can predict the mutation testing result
of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can
be taken into consideration as a light-weight alternative to mutation testing
or as a preceding, less costly step to that.Comment: EASE 201
Analyzing the test process using structural coverage
A large, commercially developed FORTRAN program was modified to produce structural coverage metrics. The modified program was executed on a set of functionally generated acceptance tests and a large sample of operational usage cases. The resulting structural coverage metrics are combined with fault and error data to evaluate structural coverage. It was shown that in the software environment the functionally generated tests seem to be a good approximation of operational use. The relative proportions of the exercised statement subclasses change as the structural coverage of the program increases. A method was also proposed for evaluating if two sets of input data exercise a program in a similar manner. Evidence was provided that implies that in this environment, faults revealed in a procedure are independent of the number of times the procedure is executed and that it may be reasonable to use procedure coverage in software models that use statement coverage. Finally, the evidence suggests that it may be possible to use structural coverage to aid in the management of the acceptance test processed
Annotated bibliography of software engineering laboratory literature
An annotated bibliography is presented of technical papers, documents, and memorandums produced by or related to the Software Engineering Laboratory. The bibliography was updated and reorganized substantially since the original version (SEL-82-006, November 1982). All materials were grouped into eight general subject areas for easy reference: (1) The Software Engineering Laboratory; (2) The Software Engineering Laboratory: Software Development Documents; (3) Software Tools; (4) Software Models; (5) Software Measurement; (6) Technology Evaluations; (7) Ada Technology; and (8) Data Collection. Subject and author indexes further classify these documents by specific topic and individual author
Annotated bibliography of software engineering laboratory literature
An annotated bibliography of technical papers, documents, and memorandums produced by or related to the Software Engineering Laboratory is given. More than 100 publications are summarized. These publications cover many areas of software engineering and range from research reports to software documentation. This document has been updated and reorganized substantially since the original version (SEL-82-006, November 1982). All materials have been grouped into eight general subject areas for easy reference: the Software Engineering Laboratory; the Software Engineering Laboratory-software development documents; software tools; software models; software measurement; technology evaluations; Ada technology; and data collection. Subject and author indexes further classify these documents by specific topic and individual author
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