In this work, the antlion optimization (ALO) is employed due to its efficiency and wide applicability to estimate the parameters of four modified models of the basic constructive cost model (COCOMO) model. Three tests are carried out to show the effectiveness of ALO: first, it is used with Bailey and Basili dataset for the basic COCOMO Model and Sheta's Model 1 and 2, and is compared with the firefly algorithm (FA), genetic algorithms (GA), and particle swarm optimization (PSO). Second, parameters of Sheta's Model 1 and 2, Uysal's Model 1 and 2 are optimized using Bailey and Basili dataset; results are compared with directed artificial bee colony algorithm (DABCA), GA, and simulated annealing (SA). Third, ALO is used with Basic COCOMO model and four large datasets, results are compared with hybrid bat inspired gravitational search algorithm (hBATGSA), improved BAT (IBAT), and BAT algorithms. Results of Test1 and Test2 show that ALO outperformed others, as for Test3, ALO is better than BAT and IBAT using MAE and the number of best estimations. ALO proofed achieving better results than hBATGSA for datasets 2 and 4 out of the four datasets explored in terms of MAE and the number of best estimates.

Software Reliability is considered to be an essential part of software systems; it involves measuring the system's probability of having failures; therefore, it is strongly related to Software Quality. Software Reliability Growth Models are used to indicate the expected number of failures encountered after the software has been completed, it is also an indicator of the software readiness to be delivered. This paper presents a study of selecting the best Software Reliability Growth Model according to the dataset at hand. Several Comparison Criteria are used to yield a ranking methodology to be used in pointing out best models. The Social Spider Algorithm (SSA), one of the newly introduced Swarm Intelligent Algorithms, is used for estimating the parameters of the SRGMs for two datasets. Results indicate that the use of SSA was efficient in assisting the process of criteria weighting to find the optimal model and the best overall ranking of employed models.

With the huge advance in artificial intelligence and the rapid development of intelligent swarm algorithms, the exploration of facility layout problem (FLP) with its non-deterministic polynomial-time (NP-Hard) nature has gained much more attention. The squirrel search algorithm is one of the swarm algorithms that is known for its effective gliding feature that provides cheap exploration of lengthy distances. In this work, Msqrl algorithm is presented as a modification of squirrel search algorithm to be capable of handling permutation-specific FLP. The modification is done by introducing two new operators: Msqrl-Exchange and Msqrl-Winter. It is used to investigate the effectiveness in finding acceptable solutions to variable-size, single-row FLPs in a fast and efficient manner. Tests included small and large benchmark instances for comparisons. Outcomes show that Msqrl was able to improved quite a few previously found solutions by acting efficiently and converging rapidly to solutions. It outperformed both semidefinite programming and cuckoo optimization in finding optimal solutions in an acceptable number of iterations and relatively small population sizes.

This work aims to study and explore the use of Gene Expression Programming (GEP) in solving on-line Bin-Packing problem. The main idea is to show how GEP can automatically find acceptable heuristic rules to solve the problem efficiently and economically. One dimensional Bin-Packing problem is considered in the course of this work with the constraint of minimizing the number of bins filled with the given pieces. Experimental Data includes instances of benchmark test data taken from Falkenauer (1996) for One-dimensional Bin-Packing Problems. Results show that GEP can be used as a very powerful and flexible tool for finding interesting compact rules suited for the problem. The impact of functions is also investigated to show how they can affect and influence the success of rates when they appear in rules. High success rates are gained with smaller population size and fewer generations compared to a previous work performed using Genetic Programming. ‫مدائل‬ ‫حل‬ ‫في‬ ‫الجيني‬ ‫بالتعبير‬ ‫البرمجة‬ ‫تطبيق‬‫الصناديق‬ ‫تعليب‬ ‫الداعاتي‬ ‫اكخم‬ ‫نجالء‬ ‫ياضيات‬ ‫الخ‬ ‫و‬ ‫الحاسهب‬ ‫عمهم‬ ‫كمية‬ ‫السهصل‬ ‫جامعة‬ / ‫البحث‬ ‫استالم‬ ‫تاريخ‬ : 42 / 9 / 4104 ‫البحث:‬ ‫قبول‬ ‫تاريخ‬ 01 / 0 / 4100 ‫الخالصة‬ ‫يهجف‬ ‫هحا‬ ‫البحث‬ ‫(مأل‬ ‫التعميب‬ ‫مدالة‬ ‫حل‬ ‫في‬ ‫الجيشي‬ ‫بالتسثيل‬ ‫البخمجة‬ ‫يقة‬ ‫طخ‬ ‫استخجام‬ ‫استكذاف‬ ‫و‬ ‫اسة‬ ‫در‬ ‫الى‬ ( ‫ة‬ ‫السباشخ‬ ‫ية‬ ‫الفهر‬ ‫يقة‬ ‫بالطخ‬ ‫الرشاديق)‬ ‫تعميب‬ ‫او‬ ‫العمب‬ on-line ‫الكيفية‬ ‫تهضيح‬ ‫عمى‬ ‫االساسية‬ ‫ة‬ ‫الفكخ‬ ‫تكد‬ ‫وتخ‬ .) ( ‫يقة‬ ‫طخ‬ ‫فيها‬ ‫تتسكن‬ ‫التي‬ GEP ‫تمق‬ ‫بذكل‬ ‫وجيجة‬ ‫مقبهلة‬ ‫حجسية‬ ‫اعج‬ ‫قه‬ ‫ايجاد‬ ‫من‬ ) ‫كفهء‬ ‫بذكل‬ ‫السدالة‬ ‫حل‬ ‫ألجل‬ ‫ائي‬ ‫العمب‬ ‫او‬ ‫الرشاديق‬ ‫عجد‬ ‫تقميل‬ ‫قيج‬ ‫بهجهد‬ ‫البحث‬ ‫هحا‬ ‫مدار‬ ‫في‬ ‫احج‬ ‫اله‬ ‫البعج‬ ‫ذات‬ ‫التعميب‬ ‫مدالة‬ ‫اخح‬ ‫تم‬ ‫اقترادي.‬ ‫و‬ ‫العالم‬ ‫من‬ ‫مأخهذة‬ ‫قياسية‬ ‫نسهذجية‬ ‫حاالت‬ ‫يبية‬ ‫التجخ‬ ‫البيانات‬ ‫تتزسن‬ ‫السعطاة.‬ ‫بالقطع‬ ‫تسأل‬ ‫التي‬ Falkenauer ‫في‬ ‫عام‬ 6991 ‫الت‬ ‫لسدائل‬ ( ‫يقة‬ ‫طخ‬ ‫استخجام‬ ‫باإلمكان‬ ‫انه‬ ‫الشتائج‬ ‫تبين‬ ‫البعج.‬ ‫احادية‬ ‫عميب‬ GEP ‫نة‬ ‫ومخ‬ ‫ججا‬ ‫قهية‬ ‫كأداة‬ ) ‫إلظهار‬ ‫اعج‬ ‫القه‬ ‫في‬ ‫السدتخجمة‬ ‫ال‬ ‫الجو‬ ‫تأثيخ‬ ‫استقراء‬ ‫ايزا‬ ‫البحث‬ ‫تشاول‬ ‫السدالة.‬ ‫تالئم‬ ‫ومفيجة‬ ‫محكسة‬ ‫اعج‬ ‫قه‬ ‫إليجاد‬ ‫القاعجة‬ ‫تكهين‬ ‫في‬ ‫تذارك‬ ‫حين‬ ‫الشجاح‬ ‫ندب‬ ‫عمى‬ ‫ونفهذها‬ ‫تأثيخها‬ ‫كيفية‬ ‫نجاح‬ ‫ندب‬ ‫استحرال‬ ‫تم‬ ‫وقج‬ ‫القانهن.‬ ‫او‬ ‫يقة‬ ‫طخ‬ ‫فيها‬ ‫استخجمت‬ ‫سابقة‬ ‫اعسال‬ ‫مع‬ ‫نتها‬ ‫مقار‬ ‫عشج‬ ‫االجيال‬ ‫من‬ ‫اصغخ‬ ‫وعجد‬ ‫اقل‬ ‫سكانية‬ ‫كثافة‬ ‫باستخجام‬ ‫ججا‬ ‫عالية‬ ( ‫الجيشية‬ ‫البخمجة‬ Genetic Programming .) ‫المفتاحية‬ ‫الكلمات‬ ‫البخمجة‬ ، ‫التعميب‬ ‫مدالة‬ ، ‫الجيشي‬ ‫بالتسثيل‬ ‫البخمجة‬ : ‫الجيشية.‬

This research aims to provide a practical work on the principle of the Extreme Programming (XP) which is a type of the Agile Software Development Methods which is used in the generation of test-cases using the design information. The resources utilized in the design information presented here are the design diagrams generated using the Unified Modeling Language (UML), as they are considered to be the most commonly used modeling language in these days, and also the newest. These UML diagrams are used to automatically develop a set of high quality test cases which are then used to test the system's code after being written. The main idea of this work is based on reducing the effort of the testing stage which costs more than 50% of the resources allocated for the whole development process; this cost may include the financial cost, the cost of the resources allocated for the project, and the timeline of the project. In this work, enhancements have been made to the concept of Gene Expression Programming to ensure the generation of high quality the test cases that are generated automatically, and a solution has been presented for the parallel paths and the loop paths problems that are found in the design.