Developing an Embedded Model for Test suite prioritization process to optimize consistency rules for inconsistencies detection and model changes

Software form typically contains a lot of contradiction and uniformity checkers help engineers find them. Even if engineers are willing to tolerate inconsistencies, they are better off knowing about their existence to avoid follow-on errors and unnecessary rework. However, current approaches do not detect or track inconsistencies fast enough. This paper presents an automated approach for detecting and tracking inconsistencies in real time (while the model changes). Engineers only need to define consistency rules-in any language-and our approach automatically identifies how model changes affect these consistency rules. It does this by observing the behavior of consistency rules to understand how they affect the model. The approach is quick, correct, scalable, fully automated, and easy to use as it does not require any special skills from the engineers using it. We use this model to define generic prioritization criteria that are applicable to GUI, Web applications and Embedded Model. We evolve the model and use it to develop a unified theory. Within the context of this model, we develop and empirically evaluate several prioritization criteria and apply them to four standalone GUI and three Web-based applications, their existing test suites and mainly embedded systems. In this model we only run our data collection and test suite prioritization process on seven programs and their existing test suites. An experiment that would be more readily generalized would include multiple programs of different sizes and from different domains. We may conduct additional empirical studies with larger EDS to address this threat each test case has a uniform cost of running (processor time) monitoring (human time); these assumptions may not hold in practice. Second, we assume that each fault contributes uniformly to the overall cost, which again may not hold in practice. GJCST-C Classification : D.2.5 Developing an Embedded Model for Test suite prioritization process to optimize consistency rules for inconsistencies detection and model changes Strictly as per the compliance and regulations of: Developing an Embedded Model for Test Suite Prioritization Process to Optimize Consistency Rules for Inconsistencies Detection and Model Changes Developing an Embedded Model for Test Suite Prioritization Process to Optimize Consistency Rules for Inconsistencies Detection and Model Changes Muzammil H Mohammed α & Sultan Aljahdali σ Abstract Software form typically contains a lot of Software form typically contains a lot of contradiction and uniformity checkers help engineers find them. Even if engineers are willing to tolerate inconsistencies, they are better off knowing about their existence to avoid follow-on errors and unnecessary rework. However, current approaches do not detect or track inconsistencies fast enough. This paper presents an automated approach for detecting and tracking inconsistencies in real time (while the model changes). Engineers only need to define consistency rules in any language and our approach automatically identifies how model changes affect these consistency rules. It does this by observing the behavior of consistency rules to understand how they affect the model. The approach is quick, correct, scalable, fully automated, and easy to use as it does not require any special skills from the engineers using it. We use this model to define generic prioritization criteria that are applicable to GUI, Web applications and Embedded Model. We evolve the model and use it to develop a unified theory. Within the context of this model, we develop and empirically evaluate several prioritization criteria and apply them to four stand-alone GUI and three Web-based applications, their existing test suites and mainly embedded systems. In this model we only run our data collection and test suite prioritization process on seven programs and their existing test suites. An experiment that would be more readily generalized would include multiple programs of different sizes and from different domains. We may conduct additional empirical studies with larger EDS to address this threat each test case has a uniform cost of running (processor time) monitoring (human time); these assumptions may not hold in practice. Second, we assume that each fault contributes uniformly to the overall cost, which again may not hold in practice.