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Artykuły w czasopismach na temat „Genetic software engineering”

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Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 20 lutego 2023

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1

Jain, Rachna, i Arun Sharma. "ASSESSING SOFTWARE RELIABILITY USING GENETIC ALGORITHMS". Journal of Engineering Research [TJER] 16, nr 1 (9.05.2019): 11. http://dx.doi.org/10.24200/tjer.vol16iss1pp11-17.

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The role of software reliability and quality improvement is becoming more important than any other issues related to software development. To date, we have various techniques that give a prediction of software reliability like neural networks, fuzzy logic, and other evolutionary algorithms. A genetic algorithm has been explored for predicting software reliability. One of the important aspects of software quality is called software reliability, thus, software engineering is of a great place in the software industry. To increase the software reliability, it is mandatory that we must design a model that predicts the fault and error in the software program at early stages, rectify them and then increase the functionality of the program within a minimum time and in a low cost. There exist numerous algorithms that predict software errors such as the Genetic Algorithm, which has a very high ability to predict software bugs, failure and errors rather than any other algorithm. The main purpose of this paper is to predict software errors with so precise, less time-consuming and cost-effective methodology. The outcome of this research paper is showing that the rates of applied methods and strategies are more than 96 percent in ideal conditions.

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Jóźwiak, Lech, i Adam Postuła. "Genetic engineering versus natural evolution". Journal of Systems Architecture 48, nr 1-3 (wrzesień 2002): 99–112. http://dx.doi.org/10.1016/s1383-7621(02)00094-2.

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Striuk, Andrii. "Formation of software design skills among software engineering students". Educational Dimension 58 (15.06.2022): 1–21. http://dx.doi.org/10.31812/educdim.4519.

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The study focuses on one of the mobile-oriented environment competence components for software engineering (SE) students. It has been demonstrated that the implementation of the higher education standard for SE bachelors has generated a number of issues in terms of ensuring training quality, principally due to a lack of specification for both skills and learning outcomes. Designing a precise framework of professional competencies for SE bachelors is one method to overcome these issues. The research examines methods for developing K14 (the ability to participate in software design, including modeling (formal description) of its structure, behavior, and working processes), a critical particular professional competency for future software engineers. Recommendations for software design teaching techniques, learning content, modeling and design tools, and assessment of the level of formation of relevant competence are developed based on a historical and genetic review of software design training among SE students in the UK, USA, Canada, Australia, New Zealand, and Singapore. The industrial-style software design training (studio training) is used as an example. The transition from architectural to detailed design, as well as project implementation, are discussed. The study's future prospects include substantiating the third engineering component of SE – software construction (after requirements engineering and design engineering).

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Barman, Shohag, Hira Lal Gope, M. M. Manjurul Islam, Md Mehedi Hasan i Umme Salma. "Clustering Techniques for Software Engineering". Indonesian Journal of Electrical Engineering and Computer Science 4, nr 2 (1.11.2016): 465. http://dx.doi.org/10.11591/ijeecs.v4.i2.pp465-472.

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<p>Software industries face a common problem which is the maintenance cost of industrial software systems. There are lots of reasons behind this problem. One of the possible reasons is the high maintenance cost due to lack of knowledge about understanding the software systems that are too large, and complex. Software clustering is an efficient technique to deal with such kind of problems that arise from the sheer size and complexity of large software systems. Day by day the size and complexity of industrial software systems are rapidly increasing. So, it will be a challenging task for managing software systems. Software clustering can be very helpful to understand the larger software system, decompose them into smaller and easy to maintenance. In this paper, we want to give research direction in the area of software clustering in order to develop efficient clustering techniques for software engineering. Besides, we want to describe the most recent clustering techniques and their strength as well as weakness. In addition, we propose genetic algorithm based software modularization clustering method. The result section demonstrated that proposed method can effectively produce good module structure and it outperforms the state of the art methods. </p>

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Leite, J. P. B., i B. H. V. Topping. "Improved genetic operators for structural engineering optimization". Advances in Engineering Software 29, nr 7-9 (sierpień 1998): 529–62. http://dx.doi.org/10.1016/s0965-9978(98)00021-0.

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He, Renjie, i Lining Xing. "Multi-Objective Genetic Algorithm to the Optimization of Software Engineering Resources". Advanced Science Letters 7, nr 1 (30.03.2012): 639–42. http://dx.doi.org/10.1166/asl.2012.2690.

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Pandey, Abhishek, i Soumya Banerjee. "Test Suite Optimization Using Firefly and Genetic Algorithm". International Journal of Software Science and Computational Intelligence 11, nr 1 (styczeń 2019): 31–46. http://dx.doi.org/10.4018/ijssci.2019010103.

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Software testing is essential for providing error-free software. It is a well-known fact that software testing is responsible for at least 50% of the total development cost. Therefore, it is necessary to automate and optimize the testing processes. Search-based software engineering is a discipline mainly focussed on automation and optimization of various software engineering processes including software testing. In this article, a novel approach of hybrid firefly and a genetic algorithm is applied for test data generation and selection in regression testing environment. A case study is used along with an empirical evaluation for the proposed approach. Results show that the hybrid approach performs well on various parameters that have been selected in the experiments.

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AZAR, DANIELLE. "A GENETIC ALGORITHM FOR IMPROVING ACCURACY OF SOFTWARE QUALITY PREDICTIVE MODELS: A SEARCH-BASED SOFTWARE ENGINEERING APPROACH". International Journal of Computational Intelligence and Applications 09, nr 02 (czerwiec 2010): 125–36. http://dx.doi.org/10.1142/s1469026810002811.

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In this work, we present a genetic algorithm to optimize predictive models used to estimate software quality characteristics. Software quality assessment is crucial in the software development field since it helps reduce cost, time and effort. However, software quality characteristics cannot be directly measured but they can be estimated based on other measurable software attributes (such as coupling, size and complexity). Software quality estimation models establish a relationship between the unmeasurable characteristics and the measurable attributes. However, these models are hard to generalize and reuse on new, unseen software as their accuracy deteriorates significantly. In this paper, we present a genetic algorithm that adapts such models to new data. We give empirical evidence illustrating that our approach out-beats the machine learning algorithm C4.5 and random guess.

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Sundar, D. "Effective Concurrent Engineering with the Usage of Genetic Algorithms for Software Development". International Journal of Software Engineering & Applications 3, nr 5 (30.09.2012): 81–89. http://dx.doi.org/10.5121/ijsea.2012.3507.

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Lin, Jin Cherng, i Chu Ting Chang. "Genetic Algorithm and Support Vector Regression for Software Effort Estimation". Advanced Materials Research 282-283 (lipiec 2011): 748–52. http://dx.doi.org/10.4028/www.scientific.net/amr.282-283.748.

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For software developers, accurately forecasting software effort is very important. In the field of software engineering, it is also a very challenging topic. Miscalculated software effort in the early phase might cause a serious consequence. It not only effects the schedule, but also increases the cost price. It might cause a huge deficit. Because all of the different software development team has it is own way to calculate the software effort, the factors affecting project development are also varies. In order to solve these problems, this paper proposes a model which combines genetic algorithm (GA) with support vector machines (SVM). We can find the best parameter of SVM regression by the proposed model, and make more accurate prediction. During the research, we test and verify our model by using the historical data in COCOMO. We will show the results by prediction level (PRED) and mean magnitude of relative error (MMRE).

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PETRY, FREDERICK E., i BERTRAND DANIEL DUNAY. "AUTOMATIC PROGRAMMING AND PROGRAM MAINTENANCE WITH GENETIC PROGRAMMING". International Journal of Software Engineering and Knowledge Engineering 05, nr 02 (czerwiec 1995): 165–77. http://dx.doi.org/10.1142/s0218194095000095.

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Automatic programming is discussed in the context of software engineering. An approach to automatic programming is presented, which utilizes software engineering principles in the synthesis and maintenance of programs. As a simple demonstration, program-equivalent Turing machines are synthesized, encapsulated, reused, and maintained by genetic programming. Turing machines are synthesized from input-output pairs for a variety of simple problems. When a problem is solved, the solution is encapsulated and becomes part of a software library. The genetic program uses the library to solve new problems by combining library components with program primitives to synthesize new programs. When a new problem is solved or a known problem is solved more efficiently, the genetic program maintains the library so as to keep it valid and efficient.

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Striuk, A. M., i S. O. Semerikov. "Professional competencies of future software engineers in the software design: teaching techniques". Journal of Physics: Conference Series 2288, nr 1 (1.06.2022): 012012. http://dx.doi.org/10.1088/1742-6596/2288/1/012012.

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Abstract The article is devoted to one of the competence components of a mobile-oriented environment for professional and practical training of future software engineers. It is shown that the introduction of higher education standard 121 “Software Engineering” for the first (bachelor) level of higher education in Ukraine has generated a number of training quality assurance problems associated primarily with the low level of detailed competencies and program learning outcomes. By solving these problems, the detailed design of the system of professional competencies for future software engineers is developed. The article deals with the approaches to developing one of the most important special professional competences of future software engineers – the ability to participate in software design, including modeling (formal description) of its structure, behavior, and processes of functioning. Based on a historical and genetic review of the software engineering training practice of future software engineers in the USA, UK, Canada, Australia, New Zealand and Singapore, recommendations for choosing forms of training organization, selection of training content, ways of students’ and teachers’ activities in software engineering, modeling and designing tools; assessment of the appropriate competence formation level are formulated. The example of organizing design training in conditions close to industrial-studio training is considered. The problems of transition from architectural to detailed design and project implementation are shown. Prospects for further development of this study are to substantiate the third (after requirements engineering and design engineering) engineering component of software engineering – the software construction.

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Krogmann, Klaus, Michael Kuperberg i Ralf Reussner. "Using Genetic Search for Reverse Engineering of Parametric Behavior Models for Performance Prediction". IEEE Transactions on Software Engineering 36, nr 6 (listopad 2010): 865–77. http://dx.doi.org/10.1109/tse.2010.69.

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Drusinsky, Doron. "Reverse engineering concurrent UML state machines using black box testing and genetic programming". Innovations in Systems and Software Engineering 13, nr 2-3 (10.08.2017): 117–28. http://dx.doi.org/10.1007/s11334-017-0299-9.

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Hwang, Shun-Fa, i Rong-Song He. "Improving real-parameter genetic algorithm with simulated annealing for engineering problems". Advances in Engineering Software 37, nr 6 (czerwiec 2006): 406–18. http://dx.doi.org/10.1016/j.advengsoft.2005.08.002.

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Afzal, Wasif, i Richard Torkar. "On the application of genetic programming for software engineering predictive modeling: A systematic review". Expert Systems with Applications 38, nr 9 (wrzesień 2011): 11984–97. http://dx.doi.org/10.1016/j.eswa.2011.03.041.

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Costa, E. O., A. Pozo i S. R. Vergilio. "A Genetic Programming Approach for Software Reliability Modeling". IEEE Transactions on Reliability 59, nr 1 (marzec 2010): 222–30. http://dx.doi.org/10.1109/tr.2010.2040759.

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Brownlee, Alexander E. I. "Genetic Improvement @ ICSE 2021". ACM SIGSOFT Software Engineering Notes 46, nr 4 (27.10.2021): 28–30. http://dx.doi.org/10.1145/3485952.3485960.

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Following Dr. Stephanie Forrest of Arizona State University's keynote presentation there was a wide ranging discussion at the tenth international Genetic Improvement workshop, GI-2021 @ ICSE (held as part of the International Conference on Software Engineering on Sunday 30th May 2021). Topics included a growing range of target systems and appli- cations, algorithmic improvements, wide-ranging questions about how other elds (especially evolutionary computation) can inform advances in GI, and about how GI is 'branded' to other disciplines. We give a personal perspective on the workshop's proceedings, the discussions that took place, and resulting prospective directions for future research.

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Shu, Leshi, Ping Jiang, Qi Zhou, Xinyu Shao, Jiexiang Hu i Xiangzheng Meng. "An on-line variable fidelity metamodel assisted Multi-objective Genetic Algorithm for engineering design optimization". Applied Soft Computing 66 (maj 2018): 438–48. http://dx.doi.org/10.1016/j.asoc.2018.02.033.

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Alexandru, Curcă. "Implementing a Software Load Balancer with a Genetic Algorithm". Scientific Bulletin of Naval Academy XXIII, nr 2 (15.12.2020): 177–84. http://dx.doi.org/10.21279/1454-864x-20-i2-024.

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In the context of network evolution, concepts like Software Defined Networking (SDN) and Network Functions Virtualisation (NFV) appeared on the market. Network virtualization permits the implementation of routers, switches and load balancers in software and separation of control plane and data plane brings easier configuration, implementation and scalability. The monolithic design of traditional network devices can be changed by implementing new algorithms which will improve the overall system performance. An example is our Software Load Balancer with a Genetic Algorithm. The code written in Python is functional through the POX Controller and the advantages of evolutionary algorithms make this implementation an innovative solution for dynamically modified topologies.

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Wang, Bin, Yong Cheng Jiang i Jing Li. "Cloud Computing Environment Based on Web Log Mining Algorithm Implementation of Test". Advanced Materials Research 760-762 (wrzesień 2013): 1293–97. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1293.

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Software test is the important means that guarantee software quality and reliability, and in this respect,it plays the role that other method cannot replace. However software test is a complex process , it needs to consume huge manpower,material resources and time,which takes the 40%~50% of entire software development cost approximately . Paper presents the inherent in software test case designing based on genetic algorithm is using genetic algorithm to solve a set of optimization test cases, and the framework includes three parts which are test environment construction, genetic algorithm and the environment for test .

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Jiménez-Domingo, Enrique, Ricardo Colomo-Palacios i Juan Miguel Gómez-Berbís. "A Multi-Objective Genetic Algorithm for Software Personnel Staffing for HCIM Solutions". International Journal of Web Portals 6, nr 2 (kwiecień 2014): 26–41. http://dx.doi.org/10.4018/ijwp.2014040103.

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The pervasive potential of artificial intelligence techniques in business scenarios has gained momentum recently through the combination of traditional software engineering disciplines and cutting-edge computer science research areas such as neural networks or genetic algorithms. Following this approach, MORGANA is a platform to perform competence oriented personnel staffing in software projects by means of a multi-objective genetic algorithm. The system is designed to be part of global human and intellectual capital management solutions. The main goal of MORGANA is to assist software project managers, by providing a comprehensible artificial intelligence-based formal framework to optimize efficiency and improve person-role fit.

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Zhao, Liang, Wei Zhang i Wenshun Wang. "Construction Cost Prediction Based on Genetic Algorithm and BIM". International Journal of Pattern Recognition and Artificial Intelligence 34, nr 07 (16.10.2019): 2059026. http://dx.doi.org/10.1142/s0218001420590260.

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According to the analysis and prediction of engineering cost, a BIM-aided analysis method based on GA network model is proposed. First, we improve the neural network by genetic algorithm; second, according to the engineering feature vector, BIM software is used to train the GA network model; finally, the GA network model reaches a steady state, given prediction of Engineering cost. According to the experimental study of 20 high-rise residential buildings in YJW area, the experimental results show that the proposed GA model combined with BIM auxiliary analysis method can accurately and easily complete the project cost prediction.

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Bibi, Nazia, Zeeshan Anwar i Ali Ahsan. "Comparison of Search-Based Software Engineering Algorithms for Resource Allocation Optimization". Journal of Intelligent Systems 25, nr 4 (1.10.2016): 629–42. http://dx.doi.org/10.1515/jisys-2015-0016.

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AbstractA project manager balances the resource allocation using resource leveling algorithms after assigning resources to project activities. However, resource leveling does not ensure optimized allocation of resources. Furthermore, the duration and cost of a project may increase after leveling resources. The objectives of resource allocation optimization used in our research are to (i) increase resource utilization, (ii) decrease project cost, and (iii) decrease project duration. We implemented three search-based software engineering algorithms, i.e. multiobjective genetic algorithm, multiobjective particle swarm algorithm (MOPSO), and elicit nondominated sorting evolutionary strategy. Twelve experiments to optimize the resource allocation are performed on a published case study. The experimental results are analyzed and compared in the form of Pareto fronts, average Pareto fronts, percent increase in resource utilization, percent decrease in project cost, and percent decrease in project duration. The experimental results show that MOPSO is the best technique for resource optimization because after optimization with MOPSO, resource utilization is increased and the project cost and duration are reduced.

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Yao, Xiangjuan, Dunwei Gong, Bin Li, Xiangying Dang i Gongjie Zhang. "Testing Method for Software With Randomness Using Genetic Algorithm". IEEE Access 8 (2020): 61999–2010. http://dx.doi.org/10.1109/access.2020.2983762.

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(Roger) Jiao, Jianxin, Yiyang Zhang i Yi Wang. "A generic genetic algorithm for product family design". Journal of Intelligent Manufacturing 18, nr 2 (4.04.2007): 233–47. http://dx.doi.org/10.1007/s10845-007-0019-7.

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Chen, Jing. "Generation Technology of Test Cases of Object-Oriented Software". Advanced Materials Research 756-759 (wrzesień 2013): 2433–37. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.2433.

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This paper analyzed the basic object-oriented concepts from the perspective of testing. The effects of the characteristics of object-oriented software on the software testing were discussed. The class testing method of object-oriented software was put forward. This method includes tests based on the state transition diagram and data flow testing on class. A integration testing of object-oriented software was put forward based on the event-driven characteristics of object-oriented software, and a data-generating method of software test based on genetic algorithm was provided. The test case generating technology of object-oriented software was discussed, which utilized an intercalation method of branch function and regarded the genetic algorithm as the core search algorithm.

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Yang, Fu, Liu Xin i Pei Yuan Guo. "A Multi-Objective Optimization Genetic Algorithm for SOPC Hardware-Software Partitioning". Advanced Materials Research 457-458 (styczeń 2012): 1142–48. http://dx.doi.org/10.4028/scientific5/amr.457-458.1142.

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Schindler, Daniel. "Genetic Engineering and Synthetic Genomics in Yeast to Understand Life and Boost Biotechnology". Bioengineering 7, nr 4 (29.10.2020): 137. http://dx.doi.org/10.3390/bioengineering7040137.

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The field of genetic engineering was born in 1973 with the “construction of biologically functional bacterial plasmids in vitro”. Since then, a vast number of technologies have been developed allowing large-scale reading and writing of DNA, as well as tools for complex modifications and alterations of the genetic code. Natural genomes can be seen as software version 1.0; synthetic genomics aims to rewrite this software with “build to understand” and “build to apply” philosophies. One of the predominant model organisms is the baker’s yeast Saccharomyces cerevisiae. Its importance ranges from ancient biotechnologies such as baking and brewing, to high-end valuable compound synthesis on industrial scales. This tiny sugar fungus contributed greatly to enabling humankind to reach its current development status. This review discusses recent developments in the field of genetic engineering for budding yeast S. cerevisiae, and its application in biotechnology. The article highlights advances from Sc1.0 to the developments in synthetic genomics paving the way towards Sc2.0. With the synthetic genome of Sc2.0 nearing completion, the article also aims to propose perspectives for potential Sc3.0 and subsequent versions as well as its implications for basic and applied research.

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Zhuang, Ying, Zhou Hua Jiang i Yang Li. "Study on Activity Prediction of Slag in Material Engineering with Software Analysis Based on GA-BPNN". Advanced Materials Research 600 (listopad 2012): 208–13. http://dx.doi.org/10.4028/www.scientific.net/amr.600.208.

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Genetic algorithm-back propagation neural network (GA-BPNN) was used for activity prediction of slag, then activity prediction software of slag was developed by Matrix Laboratory (Matlab) and Microsoft visual C++ (VC++), and activity database of slag was established. The software is simple operation and activity of slag can be predicted accurately, almost activity of slag in the condition of different temperature and composition of slag system can be predicted. The plenty of activity data was collected by the database of software, therefore, more accurate activity data for thermodynamic and dynamics calculation of metallurgist were provided by software. The software lays a good foundation for producing more advanced steel materials.

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Sadek, Adel W., Brian L. Smith i Michael J. Demetsky. "Dynamic Traffic Assignment: Genetic Algorithms Approach". Transportation Research Record: Journal of the Transportation Research Board 1588, nr 1 (styczeń 1997): 95–103. http://dx.doi.org/10.3141/1588-12.

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Real-time route guidance is a promising approach to alleviating congestion on the nation’s highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithms (GAs) is used to solve a dynamic traffic assignment model developed for a real-world routing scenario in Hampton Roads, Virginia. The results of the GA approach are presented and discussed, and the performance of the GA program is compared with an example of commercially available nonlinear programming (NLP) software. Among the main conclusions is that GAs offer tangible advantages when used to solve the dynamic traffic assignment problem. First, GAs allow the relaxation of many of the assumptions that were needed to solve the problem analytically by traditional techniques. GAs can also handle larger problems than some of the commercially available NLP software packages.

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Yadav, Chandra Shekhar, i Raghuraj Singh. "Prediction Model for Object Oriented Software Development Effort Estimation Using One Hidden Layer Feed Forward Neural Network with Genetic Algorithm". Advances in Software Engineering 2014 (3.06.2014): 1–6. http://dx.doi.org/10.1155/2014/284531.

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The budget computation for software development is affected by the prediction of software development effort and schedule. Software development effort and schedule can be predicted precisely on the basis of past software project data sets. In this paper, a model for object-oriented software development effort estimation using one hidden layer feed forward neural network (OHFNN) has been developed. The model has been further optimized with the help of genetic algorithm by taking weight vector obtained from OHFNN as initial population for the genetic algorithm. Convergence has been obtained by minimizing the sum of squared errors of each input vector and optimal weight vector has been determined to predict the software development effort. The model has been empirically validated on the PROMISE software engineering repository dataset. Performance of the model is more accurate than the well-established constructive cost model (COCOMO).

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Zakir Khan, Muhammad, Rashid Naseem, Aamir Anwar, Ijaz ul-Haq, Saddam Hussain, Roobaea Alroobaea, Syed Sajid Ullah i Fazlullah Umar. "An Enhanced Multifactor Multiobjective Approach for Software Modularization". Mathematical Problems in Engineering 2022 (8.06.2022): 1–13. http://dx.doi.org/10.1155/2022/7960610.

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Complex software systems, meant to facilitate organizations, undergo frequent upgrades that can erode the system architectures. Such erosion makes understandability and maintenance a challenging task. To this end, software modularization provides an architectural-level view that helps to understand system architecture from its source code. For modularization, nondeterministic search-based optimization uses single-factor single-objective, multifactor single-objective, and single-factor multiobjective, which have been shown to outperform deterministic approaches. The proposed MFMO approach, which uses both a heuristic (Hill Climbing and Genetic) and a meta-heuristic (nondominated sorting genetic algorithms NSGA-II and III), was evaluated using five data sets of different sizes and complexity. In comparison to leading software modularization techniques, the results show an improvement of 4.13% in Move and Join operations (MoJo, MoJoFM, and NED).

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Silva, Paulo Renato, Ivanildo Silva Abreu, Paulo Almeida Forte i Henrique Mariano Costa do Amaral. "Genetic Algorithms for Satellite Launcher Attitude Controller Design". Inteligencia Artificial 22, nr 63 (15.05.2019): 150–61. http://dx.doi.org/10.4114/intartif.vol22iss63pp150-161.

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For proper attitude control of space-crafts conventional optimal Linear Quadratic (LQ) controllers are designed via trial-and-error selection of the weighting matrices. This time consuming method is inefficient and usually results in a high order complex controller. Therefore, this work proposes a genetic algorithm (GA) for the search problem of the attitude controller gains of a satellite launcher. The GA's fitness function considers some control features as eigenstructure, control goals and constraints. According to simulation results, the search problem of controller parameters with evolutionary algorithms was faster than usual approaches and the designed controller reached all the specifications with satisfactory time responses. These results could improve engineering tasks by speeding up the design process and reducing costs.

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Nivethitha, V., i P. M Abhinaya. "Combinatorics based problem specific software architecture formulation using multi-objective genetic algorithm". International Journal of Engineering & Technology 7, nr 1.7 (5.02.2018): 79. http://dx.doi.org/10.14419/ijet.v7i1.7.9579.

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In Software Development Process, the design of complex systems is an important phase where software architects have to deal with abstract artefacts, procedures and ideas to discover the most suitable underlying architecture. Due to uncontrolled modifications of the design and frequent change of requirements, many of the working systems do not have a proper architecture. Most of the approaches recover the architectural blocks at the end of the development process which are not appropriate to the system considered. In order to structure these systems software components compositions and interactions should be properly adjusted which is a tedious work. Search-based Software Engineering (SBSE) is an emerging area which can support the decision making process of formulating the software architecture from initial analysis models. Thus component-based architectures is articulated as a multiple optimisation problem using evolutionary algorithms. Totally different metrics is applied looking on the design needs and also the specific domain. Thus during this analysis work, an effort has been created to propose a multi objective evolutionary approach for the invention of the underlying software system architectures beside a versatile encoding structure, correct style metrics for the fitness operate to enhance the standard and accuracy of the software system design.

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Chen, Jing. "A Analysis and Research on the Methods of Object Oriented Software Testing". Advanced Materials Research 709 (czerwiec 2013): 616–19. http://dx.doi.org/10.4028/www.scientific.net/amr.709.616.

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This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

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Yu, Jinsong, Yiyu Shi, Diyin Tang, Hao Liu i Limei Tian. "Optimizing sequential diagnostic strategy for large-scale engineering systems using a quantum-inspired genetic algorithm: A comparative study". Applied Soft Computing 85 (grudzień 2019): 105802. http://dx.doi.org/10.1016/j.asoc.2019.105802.

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La, Weijia, Lanying Li, Jianda Sun, Zhiqiang Lv i Fei Guan. "Hardware/Software Partitioning of Combination of Clustering Algorithm and Genetic Algorithm". International Journal of Control and Automation 7, nr 1 (31.01.2014): 347–56. http://dx.doi.org/10.14257/ijca.2014.7.1.31.

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Dewangan, Bhupesh Kumar, Tanupriya Choudhury, Vijay Bhanudas Gujar i Sudeshna Chakraborty. "Improving software performance by automatic test cases through genetic algorithm". International Journal of Computer Applications in Technology 68, nr 3 (2022): 228. http://dx.doi.org/10.1504/ijcat.2022.10049749.

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Chakraborty, Sudeshna, Vijay Bhanudas Gujar, Tanupriya Choudhury i Bhupesh Kumar Dewangan. "Improving software performance by automatic test cases through genetic algorithm". International Journal of Computer Applications in Technology 68, nr 3 (2022): 228. http://dx.doi.org/10.1504/ijcat.2022.124946.

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Khoshgoftaar, Taghi M., i Yi Liu. "A Multi-Objective Software Quality Classification Model Using Genetic Programming". IEEE Transactions on Reliability 56, nr 2 (czerwiec 2007): 237–45. http://dx.doi.org/10.1109/tr.2007.896763.

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Costa, Eduardo Oliveira, Gustavo Alexandre de Souza, Aurora Trinidad Ramirez Pozo i Silvia Regina Vergilio. "Exploring Genetic Programming and Boosting Techniques to Model Software Reliability". IEEE Transactions on Reliability 56, nr 3 (wrzesień 2007): 422–34. http://dx.doi.org/10.1109/tr.2007.903269.

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Shyr, Casper, Andre Kushniruk, Clara D. M. van Karnebeek i Wyeth W. Wasserman. "Dynamic software design for clinical exome and genome analyses: insights from bioinformaticians, clinical geneticists, and genetic counselors". Journal of the American Medical Informatics Association 23, nr 2 (27.06.2015): 257–68. http://dx.doi.org/10.1093/jamia/ocv053.

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Abstract Background The transition of whole-exome and whole-genome sequencing (WES/WGS) from the research setting to routine clinical practice remains challenging. Objectives With almost no previous research specifically assessing interface designs and functionalities of WES and WGS software tools, the authors set out to ascertain perspectives from healthcare professionals in distinct domains on optimal clinical genomics user interfaces. Methods A series of semi-scripted focus groups, structured around professional challenges encountered in clinical WES and WGS, were conducted with bioinformaticians (n = 8), clinical geneticists (n = 9), genetic counselors (n = 5), and general physicians (n = 4). Results Contrary to popular existing system designs, bioinformaticians preferred command line over graphical user interfaces for better software compatibility and customization flexibility. Clinical geneticists and genetic counselors desired an overarching interactive graphical layout to prioritize candidate variants—a “tiered” system where only functionalities relevant to the user domain are made accessible. They favored a system capable of retrieving consistent representations of external genetic information from third-party sources. To streamline collaboration and patient exchanges, the authors identified user requirements toward an automated reporting system capable of summarizing key evidence-based clinical findings among the vast array of technical details. Conclusions Successful adoption of a clinical WES/WGS system is heavily dependent on its ability to address the diverse necessities and predilections among specialists in distinct healthcare domains. Tailored software interfaces suitable for each group is likely more appropriate than the current popular “one size fits all” generic framework. This study provides interfaces for future intervention studies and software engineering opportunities.

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Zhang, Du. "A Value-Based Framework for Software Evolutionary Testing". International Journal of Software Science and Computational Intelligence 3, nr 2 (kwiecień 2011): 62–82. http://dx.doi.org/10.4018/jssci.2011040105.

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The fundamental objective in value-based software engineering is to integrate consistent stakeholder value propositions into the full extent of software engineering principles and practices so as to increase the value for software assets. In such a value-based setting, artifacts in software development such as requirement specifications, use cases, test cases, or defects, are not treated as equally important during the development process. Instead, they will be differentiated according to how much they are contributing, directly or indirectly, to the stakeholder value propositions. The higher the contributions, the more important the artifacts become. In turn, development activities involving more important artifacts should be given higher priorities and greater considerations in the development process. In this paper, a value-based framework is proposed for carrying out software evolutionary testing with a focus on test data generation through genetic algorithms. The proposed framework incorporates general principles in value-based software testing and makes it possible to prioritize testing decisions that are rooted in the stakeholder value propositions. It allows for a cost-effective way to fulfill most valuable testing objectives first and a graceful degradation when planned testing process has to be shortened.

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Shuai, Chun-jiang. "Design of Automatic Course Arrangement System for Electronic Engineering Teaching Based on Monte Carlo Genetic Algorithm". Security and Communication Networks 2021 (29.09.2021): 1–11. http://dx.doi.org/10.1155/2021/3564722.

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In order to overcome the problems of convergence and low satisfaction in the traditional course scheduling system, a new Electronic Engineering Teaching Automatic Course Scheduling System based on the Monte Carlo genetic algorithm is proposed in this paper. The overall structure and hardware structure of the course scheduling system are designed. The hardware includes system management, course scheduling information input, course scheduling management, and course schedule query. In the software part, the Monte Carlo genetic algorithm is used to optimize the course scheduling optimization process, and a course scheduling scheme more in line with the needs of students and teachers is obtained. The experimental results show that the Monte Carlo genetic algorithm has higher convergence and higher user satisfaction compared with the traditional genetic algorithm. Therefore, it shows that the performance of the course scheduling system has been effectively improved.

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Oliveira, Samuel M. D., i Douglas Densmore. "Hardware, Software, and Wetware Codesign Environment for Synthetic Biology". BioDesign Research 2022 (2.09.2022): 1–15. http://dx.doi.org/10.34133/2022/9794510.

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Synthetic biology is the process of forward engineering living systems. These systems can be used to produce biobased materials, agriculture, medicine, and energy. One approach to designing these systems is to employ techniques from the design of embedded electronics. These techniques include abstraction, standards, modularity, automated design, and formal semantic models of computation. Together, these elements form the foundation of “biodesign automation,” where software, robotics, and microfluidic devices combine to create exciting biological systems of the future. This paper describes a “hardware, software, wetware” codesign vision where software tools can be made to act as “genetic compilers” that transform high-level specifications into engineered “genetic circuits” (wetware). This is followed by a process where automation equipment, well-defined experimental workflows, and microfluidic devices are explicitly designed to house, execute, and test these circuits (hardware). These systems can be used as either massively parallel experimental platforms or distributed bioremediation and biosensing devices. Next, scheduling and control algorithms (software) manage these systems’ actual execution and data analysis tasks. A distinguishing feature of this approach is how all three of these aspects (hardware, software, and wetware) may be derived from the same basic specification in parallel and generated to fulfill specific cost, performance, and structural requirements.

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Gen, Mitsuo, Runwei Cheng i Shumuel S. Oren. "Network design techniques using adapted genetic algorithms". Advances in Engineering Software 32, nr 9 (wrzesień 2001): 731–44. http://dx.doi.org/10.1016/s0965-9978(01)00007-2.

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Topping, B. H. V., J. Sziveri, A. Bahreinejad, J. P. B. Leite i B. Cheng. "Parallel processing, neural networks and genetic algorithms". Advances in Engineering Software 29, nr 10 (grudzień 1998): 763–86. http://dx.doi.org/10.1016/s0965-9978(97)00062-8.

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Lin, C. Y., i P. Hajela. "Design optimization with advanced genetic search strategies". Advances in Engineering Software 21, nr 3 (styczeń 1994): 179–89. http://dx.doi.org/10.1016/0965-9978(94)90020-5.

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Shihao, Sun, Yixin Zhou, Yong Wang i Wu Wang. "Study on the Macroeconomic Model Based on the Genetic Algorithm". Applied Bionics and Biomechanics 2022 (30.03.2022): 1–7. http://dx.doi.org/10.1155/2022/9448895.

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In order to design a more reliable general push time cycle prediction software for macroeconomic indicators, a set of general software is used to serve financial transactions, bulk material transactions, international trade, macro-control and other fields, so as to improve the prediction of macroeconomic indicators. Because the macro data is one-dimensional array data, the essence of the mutation algorithm is to obtain the movement direction of the mutation of data nodes, obtain the distance between the linear programming result and the original data through the least square method, and calculate the average value in the original data, After binary t -correction, it refers to the binary t -correction results of the one-dimensional matrix before the final evaluation output factor and the one-dimensional matrix after the final evaluation output factor. In this study, genetic algorithm is introduced as the core algorithm. In the algorithm efficiency verification test, the calculation model based on genetic algorithm is constructed in Matlab environment, and the data space construction mode and genetic variation mode of genetic algorithm are explored. Finally, a high-throughput macroeconomic timing prediction scheme based on genetic algorithm is designed. This scheme is more accurate than the paid full-function 10jqka software, and has a higher prediction cycle for stock price and stock index. The simulation software composed of this algorithm has the prediction function that 10jqka software cannot complete.

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