| 1. |
- Feldt, Robert, 1972, et al.
(författare)
-
Searching for test data with feature diversity
- 2017
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- There is an implicit assumption in software testing that more diverse and varied test data is needed for effective testing and to achieve different types and levels of coverage. Generic approaches based on information theory to measure and thus, implicitly, to create diverse data have also been proposed. However, if the tester is able to identify features of the test data that are important for the particular domain or context in which the testing is being performed, the use of generic diversity measures such as this may not be sufficient nor efficient for creating test inputs that show diversity in terms of these features. Here we investigate different approaches to find data that are diverse according to a specific set of features, such as length, depth of recursion etc. Even though these features will be less general than measures based on information theory, their use may provide a tester with more direct control over the type of diversity that is present in the test data. Our experiments are carried out in the context of a general test data generation framework that can generate both numerical and highly structured data. We compare random sampling for feature-diversity to different approaches based on search and find a hill climbing search to be efficient. The experiments highlight many trade-offs that needs to be taken into account when searching for diversity. We argue that recurrent test data generation motivates building statistical models that can then help to more quickly achieve feature diversity.
|
|
| 2. |
|
|
| 3. |
- Poulding, Simon, et al.
(författare)
-
Automated Random Testing in Multiple Dispatch Languages
- 2017
-
Ingår i: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation, ICST 2017. - : IEEE Computer Society. - 9781509060313 ; , s. 333-344
-
Konferensbidrag (refereegranskat)abstract
- In programming languages that use multiple dispatch, a single function can have multiple implementations, each of which may specialise the function's operation. Which one of these implementations to execute is determined by the data types of all the arguments to the function. Effective testing of functions that use multiple dispatch therefore requires diverse test inputs in terms of the data types of the input's arguments as well as their values. In this paper we describe an approach for generating test inputs where both the values and types are chosen probabilistically. The approach uses reflection to automatically determine how to create inputs with the desired types, and dynamically updates the probability distribution from which types are sampled in order to improve both the test efficiency and efficacy. We evaluate the technique on 247 methods across 9 built-in functions of Julia, a technical computing language that applies multiple dispatch at runtime. In the process, we identify three real faults in these widely-used functions. © 2017 IEEE.
|
|
| 4. |
- Poulding, Simon, et al.
(författare)
-
Generating Controllably Invalid and Atypical Inputs for Robustness Testing
- 2017
-
Ingår i: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017. - : Institute of Electrical and Electronics Engineers Inc.. - 9781509066766 ; , s. 81-84
-
Konferensbidrag (refereegranskat)abstract
- One form of robustness in a software system is its ability to handle, in an appropriate manner, inputs that are unexpected compared to those it would experience in normal operation. In this paper we investigate a generic approach to generating such unexpected test inputs by extending a framework that we have previously developed for the automated creation of complex and high-structured test data. The approach is applied to the generation of valid inputs that are atypical as well as inputs that are invalid. We demonstrate that our approach enables control of the 'degree' to which the test data is invalid or atypical, and show empirically that this can alter the extent to which the robustness of a software system is exercised during testing. © 2017 IEEE.
|
|
