In the underground market for cyber-criminals, forums are extensively utilized to establish trade... more In the underground market for cyber-criminals, forums are extensively utilized to establish trade relationships and facilitate the exchange of various illegal items, resources, and crime-related services. Consequently, these underground forums contain a wealth of crucial resources for comprehending cybercrime. Given the significant role played by underground forums in the cybercrime ecosystem, analyzing these forums can yield valuable insights into cybercriminal activities. In this study, we focus on addressing three specific issues by utilizing data collected from two distinct underground forums: BlackHat World and HackForums. These issues include: (1) Identifying the product categories by extracting information from post titles. (2) Determining the economic model of the products and calculating their revenue. (3) Assessing the trustworthiness of sellers.
Detecting Design Smells Using Machine Learning: A Case Study, 2023
Continuous development in software results in complexity and this confuses the design and program... more Continuous development in software results in complexity and this confuses the design and programming stages, which makes the maintenance of the software difficult and thus affects the quality of software. Bad smells refer to weak solutions that can lead to issues with software maintainability. These smells are common problems that arise in implementation, design, and architecture, and can be identified by using a set of metrics and their threshold values. This paper conducted multiple case studies on 9 Apache projects in order to (1) determine the most effective tool for detecting bad smells, (2) learn how to detect bad smells using the most effective tools, and (3) identify the detection strategies used by those tools. Additionally, machine learning techniques were used to identify Design Smells. The aim was to demonstrate that ML techniques can be used to identify design smells, with the created dataset being made available once our work is accepted and published.
ANALYZING AIRCRAFT LANDING DECISION MAKING THROUGH FUZZY APPROACH: A COMPARATIVE STUDY, 2023
Due to the importance of weather in people's lives, various groups have advocated for accurate cl... more Due to the importance of weather in people's lives, various groups have advocated for accurate climate information. However, weather predictions can often be unclear or ambiguous. Weather advice and information are crucial in determining the safety of landing an aircraft in aviation. To address this, Mamdani Fuzzy Logic will be used to compare two scenarios: one with three inputs (wind direction, wind speed, and visibility) and another that includes the pilot's experience to assess its impact on the landing process. A fuzzy logic-based intelligent system generates three decisions: feasible, careful, and not feasible for landing an aircraft on a runway. The difference rate between the two experiments was 68%, indicating that the pilot's experience played a significant role and forced its importance in the results.
International Journal of Software Engineering and Knowledge Engineering
Paying-off the Architectural Technical Debt by refactoring the flawed code is important to contro... more Paying-off the Architectural Technical Debt by refactoring the flawed code is important to control the debt and to keep it as low as possible. Project Managers tend to delay paying off this debt because they face difficulties in comparing the cost of the refactoring against the benefits gained. These managers need to estimate the cost and the efforts required to conduct these refactoring activities as well as to decide which flaws have higher priority to be refactored. Our research is based on a dataset used by other researchers that study the technical debt. It includes more than 18,000 refactoring operations performed on 33 apache java projects. We applied the COCOMO II:2000 model to calculate the refactoring cost in person-months units per release. Furthermore, we investigated the correlation between the refactoring efforts and two static code metrics of the refactored code. The research revealed a weak correlation between the refactoring efforts and the size of the project, and ...
Proceedings of the 6th International Conference on Engineering & MIS 2020
Technical Debt (TD) can be detected using different methods. TD is a metaphor that refers to shor... more Technical Debt (TD) can be detected using different methods. TD is a metaphor that refers to short-term solutions in software development, which may affect the cost of the software development life-cycle. Several tools have been developed to detect, estimate, or manage TD. TD can be indicated through smells, code comments, and software metrics. Machine learning Techniques (MLTs) are used in many software engineering topics such as fault-proneness, bug severity, and code smell. In this paper we use four internal structure metrics to identify and classify Architecture Technical Debt (ATD) risk by using MLTs. We show that MLTs can identify and classify the risk of ATD on software components to help the decision-makers to prioritizing the refactoring decisions based on the level of the risk.
International Journal of Software Engineering and Knowledge Engineering
Do developers postpone fixing Technical Debt (TD) in software systems? TD is a metaphor that refe... more Do developers postpone fixing Technical Debt (TD) in software systems? TD is a metaphor that refers to short-term decisions in software development that may affect the cost of the software development life cycle. The bad smell is an imperfect solution in the software system that negatively impacts the internal software quality and maintainability. In this paper, we will study five open-source software projects (OSSPs) that have several releases and also estimate the numbers of architecture smells (ASs), design smells (DSs), and code smells (CSs) for every release. Designite will be used to detect smells. We describe a case study conducted to explore the following: (1) What is the average smells density for architecture, design, and code smells in an OSSP? (2) Does the density of each smell type increase over multiple releases? (3) What percentage of each smell-type density is eliminated by refactoring? We collected around 2 million LOC from five OSSPs that have multiple releases fro...
In order to find the relationship between students' English ability and the students' programming... more In order to find the relationship between students' English ability and the students' programming comprehension, we conducted a survey. The survey explores if students' weakness in the English language affects the ability of the students to understand the programming with respect to the following factors: Computer Lab, lecturer, mathematics, and logical thinking. This paper analyzed the results of two surveys conducted in two Libyan universities. Results of the surveys showed that 37%, 38%, and 25% of students stated that their programming abilities were negatively affected by English, Computer Lab and Lecturer respectively. While over half of the lecturers mentioned that the students' lack of English was the main reason for their weak performance in understanding programming skills. This study found that the programming ability had a moderate correlation with the Level of English proficiency, r=0.63, for both universities. Based on English, Computer Lab and Lecturer factors, a regression model was able to explain that 45% of the variance in programming skills.
In the underground market for cyber-criminals, forums are extensively utilized to establish trade... more In the underground market for cyber-criminals, forums are extensively utilized to establish trade relationships and facilitate the exchange of various illegal items, resources, and crime-related services. Consequently, these underground forums contain a wealth of crucial resources for comprehending cybercrime. Given the significant role played by underground forums in the cybercrime ecosystem, analyzing these forums can yield valuable insights into cybercriminal activities. In this study, we focus on addressing three specific issues by utilizing data collected from two distinct underground forums: BlackHat World and HackForums. These issues include: (1) Identifying the product categories by extracting information from post titles. (2) Determining the economic model of the products and calculating their revenue. (3) Assessing the trustworthiness of sellers.
Detecting Design Smells Using Machine Learning: A Case Study, 2023
Continuous development in software results in complexity and this confuses the design and program... more Continuous development in software results in complexity and this confuses the design and programming stages, which makes the maintenance of the software difficult and thus affects the quality of software. Bad smells refer to weak solutions that can lead to issues with software maintainability. These smells are common problems that arise in implementation, design, and architecture, and can be identified by using a set of metrics and their threshold values. This paper conducted multiple case studies on 9 Apache projects in order to (1) determine the most effective tool for detecting bad smells, (2) learn how to detect bad smells using the most effective tools, and (3) identify the detection strategies used by those tools. Additionally, machine learning techniques were used to identify Design Smells. The aim was to demonstrate that ML techniques can be used to identify design smells, with the created dataset being made available once our work is accepted and published.
ANALYZING AIRCRAFT LANDING DECISION MAKING THROUGH FUZZY APPROACH: A COMPARATIVE STUDY, 2023
Due to the importance of weather in people's lives, various groups have advocated for accurate cl... more Due to the importance of weather in people's lives, various groups have advocated for accurate climate information. However, weather predictions can often be unclear or ambiguous. Weather advice and information are crucial in determining the safety of landing an aircraft in aviation. To address this, Mamdani Fuzzy Logic will be used to compare two scenarios: one with three inputs (wind direction, wind speed, and visibility) and another that includes the pilot's experience to assess its impact on the landing process. A fuzzy logic-based intelligent system generates three decisions: feasible, careful, and not feasible for landing an aircraft on a runway. The difference rate between the two experiments was 68%, indicating that the pilot's experience played a significant role and forced its importance in the results.
International Journal of Software Engineering and Knowledge Engineering
Paying-off the Architectural Technical Debt by refactoring the flawed code is important to contro... more Paying-off the Architectural Technical Debt by refactoring the flawed code is important to control the debt and to keep it as low as possible. Project Managers tend to delay paying off this debt because they face difficulties in comparing the cost of the refactoring against the benefits gained. These managers need to estimate the cost and the efforts required to conduct these refactoring activities as well as to decide which flaws have higher priority to be refactored. Our research is based on a dataset used by other researchers that study the technical debt. It includes more than 18,000 refactoring operations performed on 33 apache java projects. We applied the COCOMO II:2000 model to calculate the refactoring cost in person-months units per release. Furthermore, we investigated the correlation between the refactoring efforts and two static code metrics of the refactored code. The research revealed a weak correlation between the refactoring efforts and the size of the project, and ...
Proceedings of the 6th International Conference on Engineering & MIS 2020
Technical Debt (TD) can be detected using different methods. TD is a metaphor that refers to shor... more Technical Debt (TD) can be detected using different methods. TD is a metaphor that refers to short-term solutions in software development, which may affect the cost of the software development life-cycle. Several tools have been developed to detect, estimate, or manage TD. TD can be indicated through smells, code comments, and software metrics. Machine learning Techniques (MLTs) are used in many software engineering topics such as fault-proneness, bug severity, and code smell. In this paper we use four internal structure metrics to identify and classify Architecture Technical Debt (ATD) risk by using MLTs. We show that MLTs can identify and classify the risk of ATD on software components to help the decision-makers to prioritizing the refactoring decisions based on the level of the risk.
International Journal of Software Engineering and Knowledge Engineering
Do developers postpone fixing Technical Debt (TD) in software systems? TD is a metaphor that refe... more Do developers postpone fixing Technical Debt (TD) in software systems? TD is a metaphor that refers to short-term decisions in software development that may affect the cost of the software development life cycle. The bad smell is an imperfect solution in the software system that negatively impacts the internal software quality and maintainability. In this paper, we will study five open-source software projects (OSSPs) that have several releases and also estimate the numbers of architecture smells (ASs), design smells (DSs), and code smells (CSs) for every release. Designite will be used to detect smells. We describe a case study conducted to explore the following: (1) What is the average smells density for architecture, design, and code smells in an OSSP? (2) Does the density of each smell type increase over multiple releases? (3) What percentage of each smell-type density is eliminated by refactoring? We collected around 2 million LOC from five OSSPs that have multiple releases fro...
In order to find the relationship between students' English ability and the students' programming... more In order to find the relationship between students' English ability and the students' programming comprehension, we conducted a survey. The survey explores if students' weakness in the English language affects the ability of the students to understand the programming with respect to the following factors: Computer Lab, lecturer, mathematics, and logical thinking. This paper analyzed the results of two surveys conducted in two Libyan universities. Results of the surveys showed that 37%, 38%, and 25% of students stated that their programming abilities were negatively affected by English, Computer Lab and Lecturer respectively. While over half of the lecturers mentioned that the students' lack of English was the main reason for their weak performance in understanding programming skills. This study found that the programming ability had a moderate correlation with the Level of English proficiency, r=0.63, for both universities. Based on English, Computer Lab and Lecturer factors, a regression model was able to explain that 45% of the variance in programming skills.
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Papers by Mrwan BenIdris