323 research outputs found
Dynamic Mutant Subsumption Analysis using LittleDarwin
Many academic studies in the field of software testing rely on mutation
testing to use as their comparison criteria. However, recent studies have shown
that redundant mutants have a significant effect on the accuracy of their
results. One solution to this problem is to use mutant subsumption to detect
redundant mutants. Therefore, in order to facilitate research in this field, a
mutation testing tool that is capable of detecting redundant mutants is needed.
In this paper, we describe how we improved our tool, LittleDarwin, to fulfill
this requirement
Evaluating Random Mutant Selection at Class-Level in Projects with Non-Adequate Test Suites
Mutation testing is a standard technique to evaluate the quality of a test
suite. Due to its computationally intensive nature, many approaches have been
proposed to make this technique feasible in real case scenarios. Among these
approaches, uniform random mutant selection has been demonstrated to be simple
and promising. However, works on this area analyze mutant samples at project
level mainly on projects with adequate test suites. In this paper, we fill this
lack of empirical validation by analyzing random mutant selection at class
level on projects with non-adequate test suites. First, we show that uniform
random mutant selection underachieves the expected results. Then, we propose a
new approach named weighted random mutant selection which generates more
representative mutant samples. Finally, we show that representative mutant
samples are larger for projects with high test adequacy.Comment: EASE 2016, Article 11 , 10 page
A Model to Estimate First-Order Mutation Coverage from Higher-Order Mutation Coverage
The test suite is essential for fault detection during software development.
First-order mutation coverage is an accurate metric to quantify the quality of
the test suite. However, it is computationally expensive. Hence, the adoption
of this metric is limited. In this study, we address this issue by proposing a
realistic model able to estimate first-order mutation coverage using only
higher-order mutation coverage. Our study shows how the estimation evolves
along with the order of mutation. We validate the model with an empirical study
based on 17 open-source projects.Comment: 2016 IEEE International Conference on Software Quality, Reliability,
and Security. 9 page
Lightweight Visualisations of COBOL Code for Supporting Migration to SOA
In this age of complex business landscapes, many enterprises turn to Service Oriented Architecture (SOA) for aligning their IT portfolio with their business. Because of the enormous business risk involved with replacing an enterpriseâs IT landscape, a stepwise migration to SOA is required. As a first step, they need to understand and assess the current structure of their legacy systems. Based on existing reverse engineering techniques, we provide visualisations to support this process for COBOL systems and present preliminary results of an ongoing industrial case study
Towards a software evolution benchmark
Case-studies are extremely popular in rapidly evolving research disciplines such as software engineering because they allow for a quick but fair assessment of new techniques. Unfortunately, a proper experimental set-up is rarely the case: all too often case-studies are based on a single small toy-example chosen to favour the technique under study. Such lack of scientific rigor prevents fair evaluation and has disastrous consequences for the credibility of our field. In this paper, we propose to use a representative set of cases as a benchmark for comparing various techniques dealing with software evolution. We hope that this proposal will launch a consensus building process that eventually must lead to a scientifically sound validation method for researchers investigating reverse- and reengineering techniques
Automatic Deployment Space Exploration Using Refinement Transformations
To manage the complex engineering information for real-time systems, the system under development may be modelled in a high-level architecture de- scription language. This high-level information provides a basis for deployment space exploration as it can be used to generate a low-level implementation. During this deployment mapping many platform-dependent choices have to be made whose consequences cannot be easily predicted. In this paper we present an approach to the automatic exploration of the deployment space based on platform-based design. All possible solutions of a deployment step are generated using a refinement trans- formation. Non-conforming deployment alternatives are pruned as early as possible using simulation or analytical methods. We validate the feasibility of our approach by deploying part of an automotive power window optimized for its real-time be- haviour using an AUTOSAR-like representation. First results are promising and show that the optimal solution can indeed be found efficiently with our approach
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