Generating model-based test cases from natural language requirements for space application software

Júnior, Valdivino Alexandre de Santiago; Vijaykumar, Nandamudi Lankalapalli

doi:10.1007/s11219-011-9155-6

Cited by 43 publications

(19 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, this approach is still difficult to implement because of the poor understandability, ambiguity, incompleteness, and inconsistency of natural language [9]. To overcome these difficulties, a set of restrictions is commonly used.…”

Section: B Natural-language-based Scenarios Generationmentioning

confidence: 99%

Interface-based Semi-automated Testing of Software Components

Potužák

Lipka

Brada

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The component-based software development enables to construct applications from reusable components providing particular functionalities and simplifies application evolution. To ensure the correct functioning of a given component-based application and its preservation across evolution steps, it is necessary to test not only the functional properties of the individual components but also the correctness of their mutual interactions and cooperation. This is complicated by the fact that third-party components often come without source code and/or documentation of functional and interaction properties. In this paper, we describe an approach for performing rigorous semi-automated testing of software components with unavailable source code. Utilizing an automated analysis of the component interfaces, scenarios invoking methods with generated parameter values are created. When they are performed on a stable application version and their runtime effects (component interactions) are recorded, the resulting scenarios with recorded effects can be used for accurate regression testing of newly installed versions of selected components. Our experiences with a prototype implementation show that the approach has acceptable demands on manual work and computational resources.

show abstract

Section: B Natural-language-based Scenarios Generationmentioning

confidence: 99%

Interface-based Semi-automated Testing of Software Components

Potužák

Lipka

Brada

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Let us consider instance i = 1 in In the context of unit test case generation for programs developed according to the Object-Oriented Programming (OOP) paradigm, this instance can be used to generate test cases for a class that has one attribute (parameter) which can take 2 values (2 1 ), 1 attribute that can take 4 values (4 1 ), another attribute that can take 5 values (5 1 ), · · · , 1 attribute that can take 6 values (6 1 ). In the system and acceptance testing context, this same sample can be used to identify test scenarios (test objectives) in a model-based test case generation approach (Santiago Júnior 2011;Santiago Júnior and Vijaykumar 2012). In both cases, the test suites must meet the criteria of pairwise testing (t = 2) where each combination of 2 values of all parameters must be covered.…”

Section: Definition and Contextmentioning

confidence: 99%

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

Balera

Júnior

2017

J Softw Eng Res Dev

View full text Add to dashboard Cite

Background: Combinatorial Interaction Testing (CIT) approaches have drawn attention of the software testing community to generate sets of smaller, efficient, and effective test cases where they have been successful in detecting faults due to the interaction of several input parameters. Recent empirical studies show that greedy algorithms are still competitive for CIT. It is thus interesting to investigate new approaches to address CIT test case generation via greedy solutions and to perform rigorous evaluations within the greedy context. Methods: We present a new greedy algorithm for unconstrained CIT, T-Tuple Reallocation (TTR), to generate CIT test suites specifically via the Mixed-value Covering Array (MCA) technique. The main reasoning behind TTR is to generate an MCA M by creating and reallocating t-tuples into this matrix M, considering a variable called goal (ζ ). We performed two controlled experiments addressing cost-efficiency and only cost. Considering both experiments, we did 3200 executions related to 8 solutions. In the first controlled experiment, we compared versions 1.1 and 1.2 of TTR in order to check whether there is significant difference between both versions of our algorithm. In such experiment, we jointly considered cost (size of test suites) and efficiency (time to generate the test suites) in a multi-objective perspective. In the second controlled experiment we confronted TTR 1.2 with five other greedy algorithms/tools for unconstrained CIT: IPOG-F, jenny, IPO-TConfig, PICT, and ACTS. We performed two different evaluations within this second experiment where in the first one we addressed cost-efficiency (multi-objective) and in the second only cost (single objective). Results: Results of the first controlled experiment indicate that TTR 1.2 is more adequate than TTR 1.1 especially for higher strengths (5, 6). In the second controlled experiment, TTR 1.2 also presents better performance for higher strengths (5, 6) where only in one case it is not superior (in the comparison with IPOG-F). We can explain this better performance of TTR 1.2 due to the fact that it no longer generates, at the beginning, the matrix of t-tuples but rather the algorithm works on a t-tuple by t-tuple creation and reallocation into M. Conclusion: Considering the metrics we defined in this work and based on both controlled experiments, TTR 1.2 is a better option if we need to consider higher strengths (5, 6). For lower strengths, other solutions, like IPOG-F, may be better alternatives.

show abstract

“…Not all studies start, however, with already specified requirements. [28] and [29] present methods to generate model-based test cases from natural language (NL) requirements to bring RE and ST closer. Use cases can also be used to deal with ambiguities of NL requirements.…”

Section: Focus Of Research (Rq2)mentioning

confidence: 99%

What Do We Know about Alignment of Requirements Engineering and Software Testing?

Karhapää

Haghighatkhah

Oivo

2017

Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering

View full text Add to dashboard Cite

Context:The alignment of different software engineering activities for coordinated functioning and optimized product development is of great importance, particularly in industrialscale development. The link between intermediate activities has been researched extensively, but the link between requirements engineering (RE) and software testing (ST) is a relatively less explored area. Objective: The objective of this study is to aggregate, structure, and classify all existing research regarding alignment of RE and ST published by the end of 2015. Method: We conducted a systematic mapping study (SMS) and aggregated all studies relevant to our scope. The primary studies are analyzed in terms of publication trend, focus area, i.e., how alignment is supported, the application domain and benefits and challenges, methodological data, and scientific rigor and industrial relevance. Results: There is a growing interest towards the topic. Several different techniques have been identified to improve RE and ST alignment. Test generation from requirements specification has received most attention. Alignment of RE and ST is particularly important for large safety-critical domains. While many challenges have been reported, the supporting evidence for benefits is scarce. Frameworks/methods/techniques is the most frequent contribution type. Solution proposal and evaluation research were the most frequently applied research type. Case study research was the most frequently applied research method, however, almost half of the studies did not clearly report any research method. Conclusion: Despite the numerous approaches that are proposed, it is not clear what approach is suitable in what context and why. To support industry in RE and ST alignment, guidelines and tool support are needed. The supporting evidence for claimed benefits is very limited. Overall, the research area is in its early stages and an increase in both the number and rigor of empirical studies are required.

show abstract

Generating model-based test cases from natural language requirements for space application software

Cited by 43 publications

References 49 publications

Interface-based Semi-automated Testing of Software Components

Interface-based Semi-automated Testing of Software Components

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

What Do We Know about Alignment of Requirements Engineering and Software Testing?

Contact Info

Product

Resources

About