Detection of code smells using machine learning techniques combined with data-balancing methods

Code smells are prevalent issues in software design that arise when implementation or design principles are violated. These issues manifest as symptoms or anomalies in the source code. Timely identification of code smells plays a crucial role in enhancing software quality and facilitating software maintenance. Previous studies have shown that code smell detection can be accomplished through the utilization of machine learning (ML) methods. However, despite their increasing popularity, research suggests that the suitability of these methods are not always appropriate due to the problem of imbalanced data. Consequently, the effectiveness of ML models may be negatively affected. This study aims to propose a novel method for detecting code smells by employing five ML algorithms, namely decision tree (DT), k-nearest neighbors (K-NN), support vector machine (SVM), XGboost (XGB), and multi-layer perceptron (MLP). Additionally, to tackle the challenge of imbalanced data, the proposed method incorporates the random oversampling technique. Experiments were conducted in this study using four datasets that encompassed code smells, specifically god-class, data-class, long-method, and feature-envy. The experimental outcomes were evaluated and compared using various performance metrics. Upon comparing the outcomes of our models on both the balanced and original datasets, we found that the XGB model achieved the highest accuracy of 100% for detecting the data class and long method on the original datasets. In contrast, the highest accuracy of 100% was obtained for the data class and long method using DT, SVM, and XGB models on the balanced datasets. According to the empirical findings, there is significant promise in using ML techniques for the accurate prediction of code smells

Some aspects of grading Java code submissions in MOOCs

Recently, massive open online courses (MOOCs) have been offering a new online approach in the field of distance learning and online education. A typical MOOC course consists of video lectures, reading material and easily accessible tests for students. For a computer programming course, it is important to provide interactive, dynamic, online coding exercises and more complex programming assignments for learners. It is expedient for the students to receive prompt feedback on their coding submissions. Although MOOC automated programme evaluation subsystem is capable of assessing source programme files that are in learning management systems, in MOOC systems there is a grader that is responsible for evaluating students’ assignments with the result that course staff would be required to assess thousands of programmes submitted by the participants of the course without the benefit of an automatic grader. This paper presents a new concept for grading programming submissions of students and improved techniques based on the Java unit testing framework that enables automatic grading of code chunks. Some examples are also given such as the creation of unique exercises by dynamically generating the parameters of the assignment in a MOOC programming course combined with the kind of coding style recognition to teach coding standards

Interpreting Brightness Asymmetries in Transition Disks: Vortex at Dead Zone or Planet-carved Gap Edges?

Recent sub-millimeter observations show non-axisymmetric brightness distributions with a horseshoe-like morphology for more than a dozen transition disks. The most accepted explanation for the observed asymmetries is the accumulation of dust in large-scale vortices. Protoplanetary disks vortices can form by the excitation of Rossby-wave instability in the vicinity of a steep pressure gradient, which can develop at the edges of a giant planet carved gap or at the edges of an accretionally inactive zone. We studied the formation and evolution of vortices formed in these two distinct scenarios by means of two-dimensional locally isothermal hydrodynamic simulations. We found that the vortex formed at the edge of a planetary gap is short-lived, unless the disk is nearly inviscid. In contrast, the vortex formed at the outer edge of a dead zone is long-lived. The vortex morphology can be significantly different in the two scenarios: the vortex radial and azimuthal extensions are ~1.5 and ~3.5 times larger for the dead zone edge compared to gap models. In some particular cases, the vortex aspect ratios can be similar in the two scenarios, however, the vortex azimuthal extensions can be used to distinguish the vortex formation mechanisms. We calculate predictions for vortex observability in the sub-millimeter continuum with ALMA. We found that the azimuthal and radial extent of brightness asymmetry correlates with vortex formation process, within the limitations of alpha-viscosity prescription.Comment: 13 pages, 10 figures, accepted for publication in Ap

Csoporttechnológia-alapú tervezés és ütemezés támogatása diszkrét matematikai modellekkel és módszerekkel = Supporting of the Group Technology Based Design, Planning and Scheduling by Means of Discrete Mathematical Models and Methods

A kutatási témában - a kitűzött célokkal összhangban - négy területen értünk el eredményeket. A választási feladatok területén a csoporttechnológia-alapú gyártócellák kialakításához egy új matematikai módszert dolgoztunk ki, amely a fogalomhálók dinamikusan fejlődő új területéhez kapcsolódik. Az eljáráshoz szoftver is készült, amelyet irodalmi összehasonlítás alapján sikeresen teszteltünk. A termelési, illetve gyártási feladatok csoportos ütemezéséhez kapcsolódóan főként a rugalmas és igény szerinti tömeggyártás igényeit is támogatni képes, kiterjesztett ütemezési modell és szoftver kifejlesztésére törekedtünk. A munkadarabok csoportjainak diszkrét ismérvek alapján való kialakítása terén elért új eredmények a fogalomhálók elméletének továbbfejlesztését jelentik a kutatócsoport által bevezetett ún. dobozhálók tulajdonságainak és alkalmazási lehetőségeinek feltárásával. Az intenzitás alapú optimalizálási feladatok csoporttechnológiai környezetben való kitűzése és megoldása szükségessé tette, hogy kidolgozzuk a diszkrét termelési folyamatok intenzitás-alapú modellezésének és optimalizálásának egységes elméleti alapjait az ún. termelési háromszög modell felhasználásával. Az elért eredmények teljes mértékben általánosíthatók csoporttechnológiai környezetre, mivel az átlagos intenzitás fogalma éppen a mindenkori csoport reprezentásához rendelhető hozzá a termelés ill. gyártás különböző hierarchiai szintjein. A kutatási témához két PhD értekezés és számos publikáció kapcsolódik. | In the research project, in accordance with the declared objectives, we obtained results in four fields. In the field of selection tasks we elaborated a new mathematical method suitable for creating Group Technology (GT) based manufacturing cells, which is connected with the new area of concept lattices developing dynamically. A software is also elaborated and successfully tested on the basis of literature. In connection with the grouped production tasks we concentrated our efforts to develop an extended scheduling model and software capable for supporting the demands of flexible and customized mass production. In the area of creating workpiece groups on the basis of discrete attributes we obtained new results by means of revealing the properties and application possibilities of so-called "box-lattices". It means further developing the theory of concept lattices. In order to define and solve intensity (rate) based optimization tasks in GT environment, we elaborated the unified theoretical basics of intensity based discrete production processes by using the so-called "production triangle model". The results obtained so far can be generalized to GT environment in full, because the concept of average intensity can be allocated to the representative member of the given group at the different hierarchy levels of production and manufacturing, respectively. Two PhD Theses before defending procedure directly and numerous publications are connected with the project