Optimization of fuzzy analogy in software cost estimation using linguistic variables

One of the most important objectives of software engineering community has been the increase of useful models that beneficially explain the development of life cycle and precisely calculate the effort of software cost estimation. In analogy concept, there is deficiency in handling the datasets containing categorical variables though there are innumerable methods to estimate the cost. Due to the nature of software engineering domain, generally project attributes are often measured in terms of linguistic values such as very low, low, high and very high. The imprecise nature of such value represents the uncertainty and vagueness in their elucidation. However, there is no efficient method that can directly deal with the categorical variables and tolerate such imprecision and uncertainty without taking the classical intervals and numeric value approaches. In this paper, a new approach for optimization based on fuzzy logic, linguistic quantifiers and analogy based reasoning is proposed to improve the performance of the effort in software project when they are described in either numerical or categorical data. The performance of this proposed method exemplifies a pragmatic validation based on the historical NASA dataset. The results were analyzed using the prediction criterion and indicates that the proposed method can produce more explainable results than other machine learning methods.Comment: 14 pages, 8 figures; Journal of Systems and Software, 2011. arXiv admin note: text overlap with arXiv:1112.3877 by other author

Learning logic programs with negation as failure

Normal logic programs are usually shorter and easier to write and understand than definite logic programs. As a consequence, it is worth investigating their learnability, if Inductive Logic Program- ming is to be proposed as an alternative tool for software development and Software Engineering at large. In this paper we present an exten- sion of the ILP system TRACY, called TRACY-not, able to learn normal logic programs. The method is proved to be sound, in the sense that it outputs a program which is complete and consistent w.r.t.the ex- amples, and complete, in the sense that it does find a solution when it exists. Compared to learning systems based on extensionality,TRACY and TRACY not are less dependent on the kind and number of training examples, which is due to the intensional evaluation of the hypothe- ses and, for TRACY-not, to the possibility to have restricted hypothesis spaces through the use of negation

On the benefit of logic-based approach to learn pairwise comparisons

In recent years, many daily processes such as internet web searching, e-mail filtering, social media services, e-commerce have benefited from Machine Learning (ML) techniques. The implementation of ML techniques has been largely focused on black box methods where the general conclusions are not easily interpretable. Hence, the elaboration with other declarative software models to identify the correctness and completeness of the models is not easy to perform. On the other hand, the emerge of some logic-based machine learning approaches that can overcome such limitations with their white box methods has been proven to be well-suited for many software engineering tasks. In this paper, we propose the use of a logic-based approach to learn user preference in the form of pairwise comparisons. APARELL as a novel approach of inductive learning is able to model the user’s preferences in Description Logic(DL) and then build a model by generalising the concept for all examples given. This offers a rich, relational representation beyond the usual propositional domain, which is then can be used to produce a set of recommendations. A user study has been performed in our experiment to evaluate the implementation of pairwise preference recommender system when compared to a standard list interface. The result of the experiment shows that the pairwise interface was significantly better than the other interface in many ways

A Course Module On Application Logic Flaws

Software security is extremely important, and even thoroughly tested code may still have exploitable vulnerabilities. Some of these vulnerabilities are caused by logic flaws. Due to the nature of application or business logic, few automated tools can test for these types of security issues. Therefore, it is important for students to learn how to reduce the number of logic flaws when developing software, and how to test for them manually. A course module with a case study was created to teach students about this topic. Case-based teaching methods are used because it allows students to better apply learned skills to real world industrial settings, and there is a lack of case studies available for current software engineering curriculum. The course module includes an introduction, a quiz on the reading, an animated PowerPoint about the case, and a set of discussion questions. The introduction covers what logic flaws are, reducing logic flaws during software development, and how to test for them manually. The case is about eCommerce merchant software Bigcommerce using PayPal Express to collect payment. A flaw lets attackers complete an expensive order using the payment intended for a cheaper order. An animation was created to trace the HTTP interactions and back-end code representing the steps of the exploit from this case, and explain the manual testing method used to discover the exploit. A set of discussion questions has students apply this method to similar code, to find potential vulnerabilities and then fix them. This course module was taught in COMP 727 Secure Software Engineering at North Carolina A&T State University in the Spring 2015 semester. A pre-survey and post-survey on the learning objectives shows students felt they improved their knowledge and skills relating to application logic flaws. A quiz based on the reading shows students understood the material. The quality of student discussions was very high. Discussion question results were graded using a rubric, and three-quarters of the class received an 85% grade or higher. Overall, this case study was effective at teaching students about application logic flaws. It will be made available to other universities, and can be easily integrated into existing curriculum

Sistem Inferensi Fuzzy Untuk Memprediksi Prestasi Belajar Mahasiswa Berdasarkan Nilai Ujian Nasional, Tes Potensi Akademik, Dan Motivasi Belajar

Predictions based on student learning achievement motivation levels, interests, and student discipline in following lectures using fuzzy logic applications have been made. This study is a follow-up of research prediction student learning achievement based on the value of the test of academic potential, NEM, and motivational learning using fuzzy inference system Mamdani method. This research is a study of the development of computer software with data inputs in the form of value of the test of academic potential, national exam score, and levels of learning motivation, and generate output in the form of student achievement results prediction (GPA). The programming language used is MATLAB version 7.0. The Data is taken from the sample as many as 216 students i.e. students of Informatic Engineering of Engineering Faculty. Data retrieval method used is the question form and documentation. Question form method used to obtain data on students' learning motivation levels, while the method of documentation used to obtain the data value of the test of academic potential score, national exam score, and GPA up to semester gasal 2011/2012. Steps of system development through stages of fuzzyfication, inference, and the determination of output. The results of this study showed that the use of applications of fuzzy logic with Mamdani fuzzy inference method can be predicted students learning achievement based on the value of test of academic potential score, national exam score, and motivation levels. This system is engineered visually, so users can use it just by doing a drag on its visual images. Based on a regression analysis that was done, the three input variables have an influence on the learning achievements of students, so that the student is expected to increase the motivation of their learning to achieve learning achievements (GPA

Effectiveness of Using MyFPGA Platform for Teaching Digital Logic

Accompanying electric circuits and computer programming, digital logic is deemed one of the most essential parts of any Electrical and Computer Engineering curriculum, so student success in the course is critical. Furthermore, research shows that the academic performance of students is heavily dependent upon student engagement, which is believed to increase with classroom strategies such as flipped-classrooms, cooperative learning, project-based learning, and virtual labs. The University of Texas Rio Grande Valley (UTRGV) is a Hispanic serving institution with distributive campuses, where many of the students work part-time. With consideration of the special needs of our students and the latest developments in engineering education, this study focuses on our recent experience of teaching digital logical using MyFPGA, online FPGA platform. We first introduce the MyFPGA platform in this paper. Developed by one of the authors of this paper, this web-based design features I/O interfacing circuits with an Intel FPGA hardware board as well as API web services with the Intel Quartus II design software. The platform provides 24/7 real-time hardware design experience at students’ fingertips, requiring only a web browser and internet access. It exposes the students to a complete engineering design cycle that includes problem specification, block diagram design, HDL source code design, simulation and hardware verification, trouble shooting and evaluation, and reporting. We consider different cases of the platform usage in two digital logic courses. To evaluate the effectiveness of the student learning experience, data is collected using outcome assessments, student feedback and self-evaluations, instructor observations, and comparative studies. Preliminary results confirmed the effectiveness of the online digital design platform. We have also identified a few pitfalls, such as instructors’ initial reluctance in adopting the platform and students’ first perception of the platform as a pure simulation tool. Based on the studies, recommendations are made to identify the best practices in the utilization of the platform to better serve Electrical and Computer Engineering majors and secondary school students interested in the general STEM fields

Relative-fuzzy: a novel approach for handling complex ambiguity for software engineering of data mining models

There are two main defined classes of uncertainty namely: fuzziness and ambiguity, where ambiguity is ‘one-to-many’ relationship between syntax and semantic of a proposition. This definition seems that it ignores ‘many-to-many’ relationship ambiguity type of uncertainty. In this thesis, we shall use complex-uncertainty to term many-to-many relationship ambiguity type of uncertainty. This research proposes a new approach for handling the complex ambiguity type of uncertainty that may exist in data, for software engineering of predictive Data Mining (DM) classification models. The proposed approach is based on Relative-Fuzzy Logic (RFL), a novel type of fuzzy logic. RFL defines a new formulation of the problem of ambiguity type of uncertainty in terms of States Of Proposition (SOP). RFL describes its membership (semantic) value by using the new definition of Domain of Proposition (DOP), which is based on the relativity principle as defined by possible-worlds logic. To achieve the goal of proposing RFL, a question is needed to be answered, which is: how these two approaches; i.e. fuzzy logic and possible-world, can be mixed to produce a new membership value set (and later logic) that able to handle fuzziness and multiple viewpoints at the same time? Achieving such goal comes via providing possible world logic the ability to quantifying multiple viewpoints and also model fuzziness in each of these multiple viewpoints and expressing that in a new set of membership value. Furthermore, a new architecture of Hierarchical Neural Network (HNN) called ML/RFL-Based Net has been developed in this research, along with a new learning algorithm and new recalling algorithm. The architecture, learning algorithm and recalling algorithm of ML/RFL-Based Net follow the principles of RFL. This new type of HNN is considered to be a RFL computation machine. The ability of the Relative Fuzzy-based DM prediction model to tackle the problem of complex ambiguity type of uncertainty has been tested. Special-purpose Integrated Development Environment (IDE) software, which generates a DM prediction model for speech recognition, has been developed in this research too, which is called RFL4ASR. This special purpose IDE is an extension of the definition of the traditional IDE. Using multiple sets of TIMIT speech data, the prediction model of type ML/RFL-Based Net has classification accuracy of 69.2308%. This accuracy is higher than the best achievements of WEKA data mining machines given the same speech data

Electrocardiogram (ECG/EKG) using FPGA

FPGAs (Field Programmable Gate Arrays) are finding wide acceptance in medical systems for their ability for rapid prototyping of a concept that requires hardware/software co-design, for performing custom processing in parallel at high data rates and be programmed in the field after manufacturing. Based on the market demand, the FPGA design can be changed and no new hardware needs to be purchased as was the case with ASICs (Application Specific Integrated Circuit) and CPLDs (Complex Programmable Logic Device). Medical companies can now move over to FPGAs saving cost and delivering highly-efficient upgradable systems. ECG (Electrocardiogram) is considered to be a must have feature for a medical diagnostic imaging system. This project attempts at implementing ECG heart-rate computation in an FPGA. This project gave me exposure to hardware engineering, learning about the low level chips like Atmel UC3A3256 micro-controller on an Atmel EVK1105 board which is used as a simulator for generating the ECG signal, the operational amplifiers for amplifying and level-shifting the ECG signal, the A/D converter chip for analog to digital conversion of the ECG signal, the internal workings of FPGA, how different hardware components communicate with each other on the system and finally some signal processing to calculate the heart rate value from the ECG signal