Analyzing Model Dependencies for Rule-based Regression Test Selection
نویسندگان
چکیده
Unintended side effects during changes of software demand for a precise test case selection to achieve both confidence and minimal effort for testing. Identifying the change related test cases requires an impact analysis across different views, models, and tests. Model-based regression testing aims to provide this analysis earlier in the software development cycle and thus enables an early estimation of test effort. In this paper, we present an approach for model-based regression testing of business processes. Our approach analyzes change types and dependency relations between different models such as Business Process Modeling Notation (BPMN), Unified Modeling Language (UML), and UML Testing Profile (UTP) models. We developed a set of impact rules to forecast the impact of those changes on the test models prior to their implementation. We discuss the implementation of our impact rules inside a prototype tool EMFTrace. The approach has been evaluated in a project for business processes on mobile devices.
منابع مشابه
A Hybrid Regression Test Selection Technique for Object-Oriented Programs
We propose a regression test selection technique that is based on analysis of both the source code of an object-oriented program as well as the UML state machine models of the affected classes. We first construct a dependency graph model of the original program from the source code. When the program is suitably modified, the constructed model is updated to reflect the changes. Our model in addi...
متن کاملTowards Refactoring-Aware Regression Test Selection
Regression testing checks that recent project changes do not break previously working functionality. Although important, regression testing is costly when changes are frequent. Regression test selection (RTS) optimizes regression testing by running only tests whose results might be a ected by a change. Traditionally, RTS collects dependencies (e.g., on les) for each test and skips the tests, at...
متن کاملAnalyzing Conflicts and Dependencies of Rule-Based Transformations in Henshin
Rule-based model transformation approaches show two kinds of non-determinism: (1) Several rules may be applicable to the same model and (2) a rule may be applicable at several different matches. If two rule applications to the same model exist, they may be in conflict, i.e., one application may disable the other one. Furthermore, rule applications may enable others leading to dependencies. The ...
متن کاملA Novel Method for Selecting the Supplier Based on Association Rule Mining
One of important problems in supply chains management is supplier selection. In a company, there are massive data from various departments so that extracting knowledge from the company’s data is too complicated. Many researchers have solved this problem by some methods like fuzzy set theory, goal programming, multi objective programming, the liner programming, mixed integer programming, analyti...
متن کاملComparative Approach to the Backward Elimination and for-ward Selection Methods in Modeling the Systematic Risk Based on the ARFIMA-FIGARCH Model
The present study aims to model systematic risk using financial and accounting variables. Accordingly, the data for 174 companies in Tehran Stock Exchange are extracted for the period of 2006 to 2016. First, the systematic risk index is estimated using the ARFIMA-FIGARCH model. Then, based on the research background, 35 affective financial and accounting variables are simultaneously used with t...
متن کامل