Automatic generation of test sequences form EFSM models using evolutionary algorithms

Automated test data generation through evolutionary testing (ET) is a topic of interest to the software engineering community. While there are many ET-based techniques for automatically generating test data from code, the problem of generating test data from an extended finite state machine (EFSMs) is more complex and has received little attention. In this paper, we introduce a novel approach that addresses the problem of generating input test sequences that trigger given feasible paths in an EFSM model by employing an ET-based technique. The proposed approach expresses the problem as a search for input parameters to be applied to a set of functions to be called sequentially. In order to apply ET-based technique, a new fitness function is introduced to cope with the case when a test target involves calls to a set of transitions sequentially. We evaluate our approach empirically using five sets of randomly generated paths through two EFSM case studies: INRES and class 2 transport protocols. In the experiments, we apply two search techniques: a random and an ET-based which utilizes our new fitness function. Experimental results show that the proposed approach produces input test sequences that trigger all the feasible paths used with a success rate of 100%, however, the random technique failed in most cases with a success rate of 20.8%

PENINGKATAN HASIL BELAJAR SISWA MELALUI MODEL PEMBELAJARAN BLENDED LEARNING PADA MATERI EKONOMI KREATIF DI SMP DHARMA PATRA PANGKALAN SUSU

Tujuan penelitian ini untuk untuk mengetahui peningkatan hasil belajar siswa saat menerapkan model pembelajaran blended learning dengan menggunakan aplikasi google classroom pada materi ekonomi kreatif mata pelajaran IPS kelas IX di SMP Dharma Patra. Penelitian ini merupakan penelitian deskriptif kuantitatif. Populasi dalam penelitian ini adalah seluruh siswa kelas IX SMP Dharma Patra Pangkalan Susu. Sampel yang digunakan untuk penelitian ini adalah siswa kelas IX 1 sebanyak 32 orang sebagai kelas eksperimen dan kelas IX 2 sebanyak 30 orang sebagai kelas kontrol. Teknik analisis data yang diterapkan yaitu: 1) pengujian prasyarat dengan uji normalistas dan uji homogenitas; dan 2) Uji hipotesis mengunakan uji beda Independent Sample t-Test dan Dependend Sample t-Test (Paired Sample t-Test). Berdasarkan data hasil penelitian menunjukan bahwa Hasil belajar siswa dengan menerapkan model pembelajaran blended learning dengan menggunakan google classroom dalam pembelajaran ekonomi kreatif mengalami peningkatan dengan nilai rata-rata 60,25 menjadi 82,88 dan masuk dalam kategori hasil belajar siswa baik. Ada perbedaan antara hasil belajar antara kelas yang menerapkan model pembelajaran blended learning dengan menggunakan google classroom dalam pembelajaran ekonomi kreatif lebih tinggi daripada kelas yang menerapkan model pemebelajaran konvensional.The purpose of this study was to determine the improvement of student learning outcomes when applying the blended learning model using the google classroom application on the creative economy material for social studies subjects for class IX at Dharma Patra Junior High School. This research is quantitative descriptive. The population in this study were all students of class IX SMP Dharma Patra Pangkalan Susu. The sample used for this study were 32 students of class IX 1 as the experimental class and class IX 2 as many as 30 people as the control class. The data analysis techniques applied are: 1) prerequisite testing with normality test and homogeneity test; and 2) Hypothesis testing using different independent sample t-Test and Dependend Sample t-Test (Paired Sample t-Test). Based on the research data, it shows that student learning outcomes by applying the blended learning model with google classroom in creative economy learning have increased with an average value of 60.25 to 82.88 and are included in the category of good student learning outcomes. There is a difference between learning outcomes between classes that apply the blended learning model and the use of google classroom in creative economics learning which is higher than the class that applies the conventional learning model

Robustness-Driven Resilience Evaluation of Self-Adaptive Software Systems

An increasingly important requirement for certain classes of software-intensive systems is the ability to self-adapt their structure and behavior at run-time when reacting to changes that may occur to the system, its environment, or its goals. A major challenge related to self-adaptive software systems is the ability to provide assurances of their resilience when facing changes. Since in these systems, the components that act as controllers of a target system incorporate highly complex software, there is the need to analyze the impact that controller failures might have on the services delivered by the system. In this paper, we present a novel approach for evaluating the resilience of self-adaptive software systems by applying robustness testing techniques to the controller to uncover failures that can affect system resilience. The approach for evaluating resilience, which is based on probabilistic model checking, quantifies the probability of satisfaction of system properties when the target system is subject to controller failures. The feasibility of the proposed approach is evaluated in the context of an industrial middleware system used to monitor and manage highly populated networks of devices, which was implemented using the Rainbow framework for architecture-based self-adaptation

An optimal factor analysis approach to improve the wavelet-based image resolution enhancement techniques

The existing wavelet-based image resolution enhancement techniques have many assumptions, such as limitation of the way to generate low-resolution images and the selection of wavelet functions, which limits their applications in different fields. This paper initially identifies the factors that effectively affect the performance of these techniques and quantitatively evaluates the impact of the existing assumptions. An approach called Optimal Factor Analysis employing the genetic algorithm is then introduced to increase the applicability and fidelity of the existing methods. Moreover, a new Figure of Merit is proposed to assist the selection of parameters and better measure the overall performance. The experimental results show that the proposed approach improves the performance of the selected image resolution enhancement methods and has potential to be extended to other methods

Easy over Hard: A Case Study on Deep Learning

While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a)~a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b)~other researchers to repeat, improve, or even refute that original work. For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together. That deep learning system took 14 hours to execute. We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84 times faster hours than deep learning method. We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis. If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.Comment: 12 pages, 6 figures, accepted at FSE201