Developed Source Code Research Articles

Clustering source code from automated assessment of programming assignments

AbstractClustering of source code is a technique that can help improve feedback in automated program assessment. Grouping code submissions that contain similar mistakes can, for instance, facilitate the identification of students’ difficulties to provide targeted feedback. Moreover, solutions with similar functionality but possibly different coding styles or progress levels can allow personalized feedback to students stuck at some point based on a more developed source code or even detect potential cases of plagiarism. However, existing clustering approaches for source code are mostly inadequate for automated feedback generation or assessment systems in programming education. They either give too much emphasis to syntactical program features, rely on expensive computations over pairs of programs, or require previously collected data. This paper introduces an online approach and implemented tool—AsanasCluster—to cluster source code submissions to programming assignments. The proposed approach relies on program attributes extracted from semantic graph representations of source code, including control and data flow features. The obtained feature vector values are fed into an incremental k-means model. Such a model aims to determine the closest cluster of solutions, as they enter the system, timely, considering clustering is an intermediate step for feedback generation in automated assessment. We have conducted a twofold evaluation of the tool to assess (1) its runtime performance and (2) its precision in separating different algorithmic strategies. To this end, we have applied our clustering approach on a public dataset of real submissions from undergraduate students to programming assignments, measuring the runtimes for the distinct tasks involved: building a model, identifying the closest cluster to a new observation, and recalculating partitions. As for the precision, we partition two groups of programs collected from GitHub. One group contains implementations of two searching algorithms, while the other has implementations of several sorting algorithms. AsanasCluster matches and, in some cases, improves the state-of-the-art clustering tools in terms of runtime performance and precision in identifying different algorithmic strategies. It does so without requiring the execution of the code. Moreover, it is able to start the clustering process from a dataset with only two submissions and continuously partition the observations as they enter the system.

TrajPy: empowering feature engineering for trajectory analysis across domains.

Trajectories, which are sequentially measured quantities that form a path, are an important presence in many different fields, from hadronic beams in physics to electrocardiograms in medicine. Trajectory analysis requires the quantification and classification of curves, either by using statistical descriptors or physics-based features. To date, no extensive and user-friendly package for trajectory analysis has been readily available, despite its importance and potential application across various domains. We have developed TrajPy, a free, open-source Python package that serves as a complementary tool for empowering trajectory analysis. This package features a user-friendly graphical user interface and offers a set of physical descriptors that aid in characterizing these complex structures. TrajPy has already been successfully applied to studies of mitochondrial motility in neuroblastoma cell lines and the analysis of in silico models for cell migration, in combination with image analysis. The TrajPy package is developed in Python 3 and is released under the GNU GPL-3.0 license. It can easily be installed via PyPi, and the development source code is accessible at the repository:https://github.com/ocbe-uio/TrajPy/. The package release is also automatically archived with the DOI 10.5281/zenodo.3656044.

APLAN: open assembly planning framework in FreeCAD

In the latest decade, the consumer’s desire for personalisation has clearly taken of, challenging manufacturers to diversify their product portfolio while maintaining the same (or near) mass-produced prices. As a result, production is nowadays geared towards low volumes with high variety for which adaptive assembly systems are required. Hence, being able to automatically map out the assembly of complex products yields significant strategic advantages over competing manufacturers. Although a wide range of assembly sequencing algorithms have been developed in the past, researchers and companies still experience intellectual and financial thresholds to applying these software packages themselves. Either the algorithms have to be largely re-engineered from scientific literature, or are offered as part of costly commercial CAD software in the form of plugins and add-ons. However, in some cases, the developed source code is made publicly available via online collaboration platforms though in a variety of programming languages and often requiring the cumbersome installation of additional software libraries.Out of these needs, the proposed APLAN module arose: a software framework allowing developers to share their (dis)assembly planning algorithms in a centralised, uniform manner through the open-source 3D parametric modelling software FreeCAD. As impetus, several in-house developed algorithms for determining topological and geometrical (dis)assembly constraints are included as well as an academic (dis)assembly planner. Previously, such algorithms had to be executed via the terminal, after which the extensive liaison, blocking, and AND/OR graphs were outputted as text or static images. Through APLAN, users are now able to inspect and edit these graphs interactively and dynamically. To conclude, this manuscript finally reports on the benchmarking of the aforementioned connection and obstruction detectors, and their academic and industrial application for human-robot collaboration.

Novel GIS-based fuzzy TOPSIS and filtration algorithms for extra-large scale optimal solar energy sites identification

Solar energy stands as an increasingly attractive and environmentally responsible energy source, possessing the potential to meet the burgeoning power needs of the Sarawak region. This paper proposes a novel methodology that combines Geographic Information System-based fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and filtration algorithms to identify the top 100 optimal Solar Energy Sites (SES). The research encompasses the extra-large scale potential SES comprising 19,237 coordinated data points. It evaluates SES based on 12 diverse criteria spanning climatic conditions, technical feasibility, accessibility, environmental considerations, and social factors. This research employs a meticulous double-layer filtration process to systematically eliminate unsuitable locations, resulting in a refined list of 1,862 potential SES. Subsequently, the fuzzy TOPSIS algorithm has further narrowed down the selection to the top 100 SES, constituting the core results of this research. The results reveal an interesting distribution of the identified SES, which mainly clustered in 3 huge categories (Kuching-Samarahan, Sibu-Kapit-Mukah and Bintulu-Miri). The identified 100 SES possess high closeness coefficients, which range from 0.7 to 0.85, where 1 indicates the best solution of the fuzzy TOPSIS algorithm. Additionally, the paper presents a spatial distribution analysis of the top-ranking sites across various divisions and districts. To validate the results obtained from the proposed model, a solar suitability map has been developed in ArcGIS Pro. When comparing the results from the proposed model to those obtained from the solar suitability map, most SES have consistent relative percentages, with only one SES falling below 50 %. To provide an overview of the similarity between the two models, the average relative percentage is computed at 69.01 %, signifying the proximity of SES to the optimal value of the solar suitability map. Furthermore, the developed source code in this research exhibits flexibility, featuring filtration algorithms that can accommodate any raster maps. It serves as an asset for researchers interested in conducting comprehensive large-scale SES identification in future case studies.

Thermoeconomic and exergoenvironmental sustainability of a power-cooling organic Rankine cycle with ejector system

The study presents a power-cooling organic Rankine cycle with an ejector system (ORCPCES). The objective is to determine the thermodynamic, economic and sustainability of the ORCPCES from the manufacturing, commissioning and decommissioning phases. Component-wise modelling was first performed based on the exergy concept. The system simulation was carried out using a developed source code in Engineering Equation Solver (EES). The refrigerant leakages and the material component impact on the environment were evaluated at all phases of the plant life cycle. The results indicate that the evaporator cooling rate (ECR) and the power-cooling efficiency (PCE) varied from 120.8 to 153 kW and 28.87 to 34.43 % across the refrigerants with the highest PCE and exergy efficiency obtained using R1234ze. The maximum power output was obtained using R1234ze. The overall environmental impact due to the components occurred at5.1×105mPts, 5.24×105mPts and 5.4×105mPts for R245fa, R1234yf, and R1234ze, respectively, while 4.01×105mPts7.81×104mPts and9.33×105mPts was due to the working fluids in that order. The unit cost of electricity (UCOE) across the refrigerants varies from 0.181$/kWh to 0.2291$/kWh with the least UCOE, 0.181$/kWh obtained using R1234ze. The highest exergetic sustainability index of 0.56 was achieved using R1234ze.

A convolutional neural network-based approach to composite power system reliability evaluation

This paper proposes a machine learning-based approach in conjunction with Monte Carlo simulation (MCS) to improve the computation efficiency of composite power system reliability evaluation. Traditional composite system reliability evaluation approaches are computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been derived to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In this paper, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF, except in the training stage. Minimum load curtailments are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test Systems (The IEEE RTS and IEEE RTS-96) and Saskatchewan Power Corporation in Canada. Results show that the proposed approach is computationally efficient (fast and accurate) in calculating the most common composite system reliability indices. The developed source code of the proposed method is available to the community for future research and development.

2D and 3D numerical simulation of fatigue crack growth path and life predictions of a linear elastic

Abstract This paper describes implementation of the finite element method (FEM) to investigate crack growth problems in linear elastic fracture mechanics and the correlation of results with experimental and numerical data. The approach involved using two different software to compute stress intensity factors (SIFs), the crack propagation trajectory, and fatigue life estimation in two and three dimensions. According to the software, crack modeling might be run in various ways. The first is a developed source code program written in the Visual Fortran language, while the second is the widely used ANSYS Mechanical APDL 19.2 software. The fatigue crack propagation trajectory and the corresponding SIFs were predicted using these two software programs. The crack direction was investigated using the maximum circumferential stress theory, and the finite element (FE) analysis for fatigue crack growth was done for both software based on Paris's law. The predicted results in both software demonstrated the influence of holes on the crack growth trajectory and all associated stresses and strains. The study's findings agree with other experimental and numerical crack propagation studies presented in the literature that reveal similar crack propagation trajectory observations.

Deep ensemble learning-based approach to real-time power system state estimation

Power system state estimation (PSSE) is commonly formulated as weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, zero emission technologies (e.g., electric vehicles), and demand response programs. Appropriate PSSE approaches are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. This paper proposes a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and multivariate-linear regressor as meta-learner. Historical measurements and states are utilised to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data-acquisition. This paper adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. The results show that the proposed approach outperforms existing data-driven PSSE techniques. The developed source code of the proposed solution is publicly available athttps://github.com/nbhusal/Power-System-State-Estimation.

Empirical Assessment of the Quality of MVC Web Applications Returned by xGenerator

Many scholars have reported that the adoption of Model Driven Engineering (MDE) in the industry is still marginal. Real-life case studies, completed with convincing empirical data about the quality of the developed source code, is an effective way to persuade the industry that the adoption of MDE brings an actual added value. This paper reports about the assessment of the quality of the code outputted by xGenerator: a Java technology platform for the development of enterprise Web applications, which implements the MDE paradigm. Two recent papers from Aniche and his colleagues were selected to carry out the measurements. The former study is about metrics and thresholds for MVC Web applications, while the latter presents a catalog of six smells tailored to MVC Web applications. A big merit of both of these proposals is that they fix the metric thresholds by taking into account the MVC software architecture. The results of the empirical assessment, carried out on a real-life project, proved that the quality of the code is high.

A Bidirectional LSTM Language Model for Code Evaluation and Repair

Programming is a vital skill in computer science and engineering-related disciplines. However, developing source code is an error-prone task. Logical errors in code are particularly hard to identify for both students and professionals, and a single error is unexpected to end-users. At present, conventional compilers have difficulty identifying many of the errors (especially logical errors) that can occur in code. To mitigate this problem, we propose a language model for evaluating source codes using a bidirectional long short-term memory (BiLSTM) neural network. We trained the BiLSTM model with a large number of source codes with tuning various hyperparameters. We then used the model to evaluate incorrect code and assessed the model’s performance in three principal areas: source code error detection, suggestions for incorrect code repair, and erroneous code classification. Experimental results showed that the proposed BiLSTM model achieved 50.88% correctness in identifying errors and providing suggestions. Moreover, the model achieved an F-score of approximately 97%, outperforming other state-of-the-art models (recurrent neural networks (RNNs) and long short-term memory (LSTM)).

PlantDesign - Prototypical implementation of an integrated engineering tool

This contribution presents a novel integrated engineering tool PlantDesign, which harmonizes multi-disciplinary engineering tasks during the plant life cycle in process industries. Hereby the focus is on heterogeneous software interoperability and the data handover between different software and disciplines to enable a consistent data management and an integrated planning workflow during the basic and detailed engineering. PlantDesign is an assistant system for the automated, constructive design of plant modules with a high level of detail and it enables different data exchange interfaces to heterogeneous CAE software tools applied in basic and detailed engineering. This article highlights the newly developed source code converter framework enabling the source code generation in PlantDesign for different 3D CAD formats and tools, the integration of the PlantDesign software into the classical engineering workflow of process development and the first successful demonstration of the practicality of this new approach for an industrial application.

Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning based (UDTL-based) IFD problem. Although it has achieved huge development, a standard and open source code framework as well as a comparative study for UDTL-based IFD are not yet established. In this paper, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD which are rarely studied, including transferability of features, influence of backbones, negative transfer, physical priors, etc. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at \url{https://github.com/ZhaoZhibin/UDTL}.

Black-Box Testing : Analisis Kualitas Aplikasi Source Code Bank Programming

The Source Code Bank Programming application that the researchers built uses Borland Delphi which functions to support software learning activities and as a reference for software developers, especially the Delphi programming language. In the process of developing Source Code Bank Programming is still in the prototype stage and further development is still being carried out so that it can be competitively competitive in the world. It is necessary to do various evaluations of the quality of the application in order to match the expectations of the user, one of which is to analyze the quality of the application using the Black-Box Testing method. Testing the Black-Box Source Code Bank Programming uses 3 methods, namely; 1) Graph-based testing, 2) Equivalence Partitioning, and 3) Boundary Value Analysis. The research results show that graph testing, equivalence partitioning, and boundary value analysis will be able to help the process of making test cases and make testing simpler, There are errors in testing and are included in the defect-list, the test results also show that the fulfillment of user needs for public users with a value 0.90, user registers/member with a value of 1.00, students with a value of 0.90, lecturers with a value of 0.82 and admin with a value of 0.84 are classified as good because the degree of value in each user module is greater than 0.8.

Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning

In this paper we introduce constitutive artificial neural networks (CANNs), a novel machine learning architecture for data-driven modeling of the mechanical constitutive behavior of materials. CANNs are able to incorporate by their very design information from three different sources, namely stress-strain data, theoretical knowledge from materials theory, and diverse additional information (e.g., about microstructure or materials processing). CANNs can easily and efficiently be implemented in standard computational software. They require only a low-to-moderate amount of training data and training time to learn without human guidance the constitutive behavior also of complex nonlinear and anisotropic materials. Moreover, in a simple academic example we demonstrate how the input of microstructural data can endow CANNs with the ability to describe not only the behavior of known materials but to predict also the properties of new materials where no stress-strain data are available yet. This ability may be particularly useful for the future in-silico design of new materials. The developed source code of the CANN architecture and accompanying example data sets are available at https://github.com/ConstitutiveANN/CANN.

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE
 
 CÉSAR DE OLIVEIRA FERREIRA SILVA1
 
 1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: cesaroliveira.f.silva@gmail.com. 
 
 
 1 RESUMO
 
 Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.
 
 Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.
 
 
 SILVA, C. O .F
 SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE
 
 
 2 ABSTRACT
 
 Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.
 
 Keywords: cloud computing, remote sensing, hydrology, modeling.

Nonplanar Crack Growth Simulation of Multiple Cracks Using Finite Element Method

This work deals with a 2D finite element simulation of nonplanar multiple cracks using fracture and crack propagation analysis. This analysis was performed by using the developed source code software written by Visual Fortran Language. This source code includes the adaptive mesh generation utilizing the advanced front method and also the mesh refinement process. In order to correctly represent the field singularity, the quarter‐point singular elements are constructed around the tip of the crack. The crack growth criteria are used to predict the crack growth direction by utilizing the circumferential stress factor in calculating the yielding stress in elastic fracture assumptions. The stress intensity factor determination is one of the most critical procedures as it determines the crack initiation and propagation mechanism. Moreover, the stress intensity factor histories during the crack growth are measured with the use of equivalent domain integral methods. The crack path simulation and stress intensity factor calculations are compared with the literature and revealed that the results are in agreement with research carried in this domain.

Image-to-Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography.

Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the wellestablished pix2pix network (image-to-image network) is modified to fit the need for multitasking and extending it to the new image-to-images architecture. The developed source code of this multitask model is shared publicly on Github as the first attempt for providing the two-task pix2pix extension, a supervised/paired/aligned/registered image-to-images translation which would be useful in many multitask applications. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. The comparison with state-of-the-art al-gorithms along with ablation study and a demonstration video1 are provided to evaluate the efficacy and gauge the merits of the proposed approach.

APIUaaS: a reference architecture for facilitating API usage from a data analytics perspective

Source code examples are key resources for software developers to learn application programming interfaces (APIs) and to understand corresponding usage patterns. Developers usually have to utilise, evaluate and understand code examples from multiple sources, which involve heavy manually processing efforts. To reduce such efforts, there has been growing interest in developing source code mining and recommendation systems. This study proposes API usage as a service (APIUaaS), a reference architecture for facilitating API usage, which allows infrastructures to be built for recommending proper API code examples based on semi-automatic data analytics. This reference architecture contains five logical layers and six global-level architectural concerns. API queries are accepted from programmers, and corresponding code example candidates are extracted from the data sources layer. The detailed structural links between API elements and source codes are captured and stored in the data model & code assets layer. During the recommendation phase, API usages mining, clustering and ranking algorithms are enabled in the knowledge discover & intelligent model layer. Services such as code assist and bug detection are assembled in the API usage services layer. Finally, the authors evaluate APIUaaS from three perspectives: rationality, feasibility, and usability.

Implementation of a specific boundary condition for a simplified symmetric single-path CFD lung model with OpenFOAM

CFD modeling research about the lung airflow with a complete resolution and an adequate accuracy at all scales requires a great amount of computational resources due to the vast number of necessary grid elements. As a result, a common practice is to conduct simplifications that allows to manage it with ordinary computational power. In this study, the implementation of a special boundary condition in order to develop a simplified single conductive lung airway model, which exactly represents the effect of the removed airways, is presented. The boundary condition is programmed in the open-source software OpenFOAM®, and the developed source code is presented in the proper syntax. After this description, modeling accuracy is evaluated under different flow rate conditions typical of human breathing processes, including both inspiration and expiration movements. Afterward, a validation process is conducted using results of a Weibel's model (0-4 generations) simulation for a medium flow rate of 50 L/min. Finally, a comparison against the proposed boundary condition implemented in the commercial code ANSYS Fluent is made, which highlights the benefits of using the free code toolbox. The specific contribution of this paper will be to show that OpenFOAM® developed model can perform even better than other commercial codes due to a precise implementation and coupling of the default solver with the in-house functions by virtue of the open-source nature of the code.

Applying potential-based panel method for steady flow analysis across a wing with finite span

In the paper hereby, an incompressible irrotational steady flow across a submerged body with finite dimensions will be studied. For this purpose, it is necessary to solve Laplace’s differential equation about a potential function in order to obtain the conservative velocity vector field. A general solution to the problem utilizing the Green identity implies the double layer potential function at an arbitrary point not belonging to the boundary surface. The potential is expressed by source/sink and doublet singularities distributed over the body surface and a wake attached to the trailing edge. The wake ensures that the Kutta condition is fulfilled. The submerged body geometry is approximated further by quadrilateral panels in order to compute the surface integrals for each panel exactly. To form a linear non-homogenous algebraic system, it is essentially to compute each panel influence to a collocation point of interest. The obtained coefficient matrix is diagonally dominant. The system is solved iteratively by means of the Gauss-Seidel method. The goal is development of a non-proprietary source code in order to work out a solution to the stated problem. The developed source code is authentic. Auxiliary libraries have not been used. Validation case and numerical results are depicted and discussed in the paper.