دانشگاه کاشانمحاسبات نرم2322-370715120260522An iterated local search strengthened by a Q-learning-based hyper-heuristic for software modularizationAn iterated local search strengthened by a Q-learning-based hyper-heuristic for software modularization21411381010.22052/scj.2023.252654.1135FAمحجوبهتاج گرداندانشکده ریاضی، آمار و علوم کامپیوتر، دانشگاه تبریز، تبریز، ایران.حبیبایزدخواهدانشکده ریاضی، آمار و علوم کامپیوتر، دانشگاه تبریز، تبریز، ایران.شهریارلطفیدانشکده ریاضی، آمار و علوم کامپیوتر، دانشگاه تبریز، تبریز، ایران.Journal Article20230311Software comprehension plays an important role during its improvement and maintenance process. Software modularization is a key activity for recovering the software architecture, which improves software understanding. Since the software modularization problem is NP-hard, meta-heuristics such as evolutionary algorithms (EAs) are usually used to solve it. EAs are problem-dependent, and they also require considerable space and time. Recently, the use of hyper-heuristic approaches growing to obtain more generality. This paper proposes an iterated local search (ILS) strengthened by a Q-learning-based hyper-heuristic for software modularization that overcomes the limitations of EAs. In the proposed algorithm, two main components of ILS, i.e., perturbation and local search components, are intelligently selected using a Q-learning-based hyper-heuristic in each iteration. The performance of the proposed algorithm is evaluated on eleven real-world software systems of small and medium sizes. The results of the experiments demonstrate that the proposed ILS produces modularizations that have higher or equal quality compared to the quality of the modularizations obtained by selected algorithms.Software comprehension plays an important role during its improvement and maintenance process. Software modularization is a key activity for recovering the software architecture, which improves software understanding. Since the software modularization problem is NP-hard, meta-heuristics such as evolutionary algorithms (EAs) are usually used to solve it. EAs are problem-dependent, and they also require considerable space and time. Recently, the use of hyper-heuristic approaches growing to obtain more generality. This paper proposes an iterated local search (ILS) strengthened by a Q-learning-based hyper-heuristic for software modularization that overcomes the limitations of EAs. In the proposed algorithm, two main components of ILS, i.e., perturbation and local search components, are intelligently selected using a Q-learning-based hyper-heuristic in each iteration. The performance of the proposed algorithm is evaluated on eleven real-world software systems of small and medium sizes. The results of the experiments demonstrate that the proposed ILS produces modularizations that have higher or equal quality compared to the quality of the modularizations obtained by selected algorithms.https://scj.kashanu.ac.ir/article_113810_cbf0e9ed8140dd7746220f2663846c2a.pdfدانشگاه کاشانمحاسبات نرم2322-370715120260421Game theory approach in decision-making to invest in modulesGame theory approach in decision-making to invest in modules152011425110.22052/scj.2024.246500.1075FAامیرحسینیداللهیگروه مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه کاشان، کاشان، ایران.سلمانگلی بیدگلیگروه مهندسی کامپیوتر، دانشکده مهندسی برق و کامپیوتر، دانشگاه کاشان، کاشان، ایران.Journal Article20220626Cloud computing involves a variety of technologies, including networking and virtualization, to meet the new needs of users, but is vulnerable to many security threats. To provide the necessary level of security in cloud computing, decision-making on the type and number of security modules used by cloud service users and then paying the relevant fees is of particular importance. Game theory, with the ability to model the behavior of users and attackers of a supervisor and analyze the possible strategy and profitability of each, can suggest a suitable strategy for investing in the security modules of a virtual machine. In our previous work, we used game theory to analyze the decision to invest in one of the security modules for each of the actors. The purpose of this paper is to study the effect of the three parameters "different investment costs", "probability of success of the attack on the user" and "probability of success of the attack on the supervisor" and to make an appropriate decision in this situation. Based on the simulation results, it can be said that given the different values of the probability of a successful attack on a supervisor, a predetermined investment can lead to a proper Nash equilibrium. In general, at low costs or in the case of increasing the cost of investing in security, the user tends to constantly change his strategy and provide the desired security conditions. The results also show that as the probability of a successful attack on a user not investing in security increases, so does the security investment cost.Cloud computing involves a variety of technologies, including networking and virtualization, to meet the new needs of users, but is vulnerable to many security threats. To provide the necessary level of security in cloud computing, decision-making on the type and number of security modules used by cloud service users and then paying the relevant fees is of particular importance. Game theory, with the ability to model the behavior of users and attackers of a supervisor and analyze the possible strategy and profitability of each, can suggest a suitable strategy for investing in the security modules of a virtual machine. In our previous work, we used game theory to analyze the decision to invest in one of the security modules for each of the actors. The purpose of this paper is to study the effect of the three parameters "different investment costs", "probability of success of the attack on the user" and "probability of success of the attack on the supervisor" and to make an appropriate decision in this situation. Based on the simulation results, it can be said that given the different values of the probability of a successful attack on a supervisor, a predetermined investment can lead to a proper Nash equilibrium. In general, at low costs or in the case of increasing the cost of investing in security, the user tends to constantly change his strategy and provide the desired security conditions. The results also show that as the probability of a successful attack on a user not investing in security increases, so does the security investment cost.https://scj.kashanu.ac.ir/article_114251_033070b94784fb63e556fa50fd96aa04.pdf