# Key Technologies of Software Engineering Based on T-ACO Algorithm

Authors: Litao Lu
Journal: Mobile Information Systems
 (2022)

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## Abstract

The main core of software engineering key technologies is the development of software services, ensuring the scientificity, security, and stability of the application software engineering system. At present, China’s economic development urgently needs the support of software engineering technology. Based on the T-ACO algorithm, the scientificity of software engineering and the accuracy of data have been significantly improved compared with traditional software engineering technology. It plays an important role in promoting the follow-up software engineering technology. In order to effectively analyze the key technology of engineering software, an improved ant colony algorithm based on T distribution is proposed in this paper. Because the basic ant colony algorithm is easy to fall into the local optimum and the optimization accuracy is low, in the optimization process, at the beginning of the pheromone update, the introduction of the T distribution is helpful for the basic ant colony algorithm to make up for its shortcomings. Adding pheromone variables to the basic ant colony algorithm improves the diversity of the ant colony, thereby eliminating the limitations of local optimal solutions. At the same time, the T-ACO algorithm also improves the search accuracy and convergence speed of automatic data generation in software engineering. In this paper, the performance of the T-ACO algorithm is simulated by experiments. Experimental analysis shows that when the population size is small, the T-ACO algorithm may sometimes not converge to the optimal solution, but when the population size is large (≥50), the T-ACO algorithm may converge to the optimal solution. It can realize the coverage of the total path by the output test case set. While the other two algorithms can achieve full path coverage, they are not stable, resulting in an average coverage between 90% and 100%. The T-ACO algorithm not only has good accuracy in creating test case sets, but also has good algorithm performance, and it is suitable as a multipath test case creation algorithm.


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## Body

## 1. Introduction

The rise of Internet technology has brought another change to the development of computer science and technology. Therefore, people’s life and production methods have undergone tremendous changes, and society is evolving toward the era of big data. In the era of big data, the development of core technologies of software engineering presents three major trends: intelligence, transparency, and integration. On the one hand, intelligence must emerge through associated software and programs. On the other hand, the software engineering technology continuously exhibits the characteristics of openness and interactivity through the network operator in the development process. This also promotes the development of integrated engineering software technology based on open R&D and information. Software engineering is the engineering research object of software development, among which software testing is an important part of software engineering. This directly affects the development prospects of engineering software. At present, with the continuous expansion of software scale, traditional software testing methods can no longer meet the actual needs, and software automation testing has become the focus of current research. For automated testing, the key lies in the automatic creation of test cases. After years of research by researchers, some achievements have been made in this regard, but there is still a gap in the actual demand. To sum up, research on the automatic generation of test hypotheses has certain scientific significance for software testing and even mechanical software development.In recent years, the development of service-oriented software engineering has been accelerated. The purpose of Brambilla et al. was to provide an agile and flexible tool to introduce the world of Model Driven Software Engineering (MDSE) to quickly understand its fundamentals and techniques. And he chose the appropriate MDSE toolset according to his needs, so that he can start to benefit from MDSE immediately [1]. The aim of Kamthan was a technological renaissance of software engineering education from a human and societal perspective [2]. Lemos et al. complemented a previous roadmap paper on software engineering for adaptive systems, covering a different set of topics. These topics are related to guarantees, namely, permanent guarantees, combination and decomposition of guarantees, and guarantees derived from control theory [3]. Briand et al. focused on problems identified in collaboration with industrial partners and driven by the specific needs of specific domains and development projects [4]. Javier et al. introduced the software process and modeling language approach and also presented a metamodel with a proposed language [5]. In order to manage a large number of interview resources of different majors and courses conveniently, scientifically, and effectively and provide them to teachers and students to share, Zhang and Lv developed a set of an interview training system based on a Web environment. The system is based on the Java EE platform and adopts a three-tier architecture. The display layer is responsible for data display, the control layer is responsible for data logic processing, and the data layer is responsible for data CRUD operations [6]. However, the engineering technology is not combined with the T-ACO algorithm, and related scholars have studied the characteristics of the T-ACO algorithm.At the same time, more and more researchers have optimized the existing ant colony algorithm. For optimizing engineering designs and structures, the adaptability of metaheuristics is rapidly increasing. The urgent need for fuel-efficient designs for lightweight construction vehicles is also a rising demand from different industries. Yildiz and Mehta contributed to both fields by using the hybrid Taguchi salp swarm algorithm-Nelder-Mead (HTSSA-NM) and manta ray foraging optimization (MRFO) algorithms to optimize the structure and shape of automotive brake pedals [7]. Yldz et al. focus on minimizing product cost through the use of the newly developed Political Optimization Algorithm (POA), Archimedes Optimization Algorithm (AOA), and Levy Flight Algorithm (LFA) during product development. The search capability and computational efficiency of POA for optimizing vehicle structures are investigated. By examining the obtained results, we demonstrate that POA significantly outperforms other recent well-known metaheuristics [8]. Vehicle component design is critical for developing vehicle prototypes, as optimizing components can reduce costs and improve the performance of vehicle systems. Yldz et al. demonstrate the shape design of vehicle mounts by using a newly invented metaheuristic. The new optimizer is called the Ecogeography-Based Optimization Algorithm (EBO). The study found that compared with other optimizers such as the balanced optimization algorithm, the marine predator algorithm, and the slime mold algorithm, the design results obtained by EBO were better [9]. Aiming at the difficulties of traditional ant colony optimization, You et al. proposed an ant colony system based on a dynamic search (DSACS) strategy for the mobile robot path planning problem [10]. However, the performance of these algorithms needs to be further improved.The automatic test case generation method of T-ACO algorithm proposed in this paper has achieved certain results in solving the problem of automatic test case generation. It has also achieved certain improvements in performance. Through the analysis of the experimental results of the triangle classification program, the T-ACO algorithm is superior to the other two comparison algorithms in terms of the number of iterations, the average running time, and the success rate. The innovation of this paper is that it combines software engineering with the T-ACO algorithm. It introduces the theory and related methods of the ACO algorithm in detail and also provides a corresponding analysis of the Student’s t distribution.


## 2. Particle Swarm Optimization Method Based on T Distribution

### 2.1. Student’s T Distribution

The Gaussian operator has been used for a long time. It was first applied to the evolution strategy and gradually improved by other algorithms. In order to generate a wider range of variation, after that, some scholars replaced the Gaussian operator with the Cauchy operator and applied it to the optimization algorithm. It is undeniable that both operators have good mutation performance. Due to the large difference in variance between the two operators, the local search effect of Gaussian distribution is better and the global search effect of Cauchy distribution is better. For this reason, many scholars have thought of using the convolutional combination to obtain the average operator to balance the search ability, but after demonstrating that the average operator is equivalent to Cauchy in high-dimensional computing, it has no good practical effect. The emergence of Student’s t distribution can just synthesize the variation advantages of each division. Experiments have verified that this distribution has a good effect on balancing the mutation ability of global exploration and local fine search. Therefore, we use dynamic adaptive Student’s t-step to synthesize the variation effect [11, 12].The Student’s T distribution was proposed in 1908, mainly to solve the problem that the sample size is small and the population variance is unknown, which makes it impossible to use Gaussian distribution data to model [13]. It ushered in a new era of small sample statistics. m is the t distribution with parameter degrees of freedom, and the probability density G function is:(1)Gtz=Γm+1/2mπΓm/21+z2m−m+1/2,−∞<z<+∞,tm=1=D0,1 becomes a Cauchy distribution; if m≤2 has no variance; if m>2, the variance is m/m−2, tm⟶∞⟶M0,1. Therefore, two edge special cases of distributions are the Cauchy and Gaussian distributions, where t is 1 and t approaches infinity, respectively. In this regard, it is understood that choosing an appropriate degree of freedom m will take full advantage of the Gaussian and Cauchy distributions. The t-distribution variance also links Gaussian and Cauchy variables for a better mutation operator [14], as shown in Figure 1.Figure 1
t-distribution plot for different degrees of freedom.


### 2.2. Ant Colony Algorithm

The foraging behavior of ants is a cooperative process. On the way from the nest to the food source, ants secrete a substance, which other ants depend on, called pheromone [15, 16]. In the process of finding a path, ants can find the presence and strength of this substance and use it to judge the chosen path. Under natural conditions, pheromones also evaporate at a certain rate. The foraging behavior of ants is a self-organizing behavior, which can quickly find the shortest food path through cooperation. Figures 2–5 show the process of ants looking for food in reality.Figure 2
There are no obstacles between the nest and the food source.Figure 3
Place barriers between ant nests and food sources.Figure 4
Ants bypass obstacles to find food.Figure 5
Ants choose short paths.Assuming that there arez ants in total, Xz, Yz represent the number of ants passing on the X bridge and the Y bridge, respectively (as shown in Figure 6). Then whether the z + 1 ant will choose the X bridge or the Y bridge, we need to use the probability method to express this possibility. If we use Gx to represent the probability of choosing the X bridge, and Gy to represent the probability of choosing the N bridge, then:(2)Gxz=Xz+ldXz+ld+Yz+ld,Gyz=1−Gxz.Figure 6
Asymmetric double bridge experiment.In the formula,l represents the probability of random selection, and d represents the influence coefficient of the released pheromone concentration on the ants’ path selection. If Gx>Gy, then the probability of the z + 1th ant choosing X bridge is relatively high. If Gx<Gy, then the probability of the z + 1th ant to choose the Y bridge is relatively high.Before introducing the ant system model, let us look at the traveling salesman problem. The core problem of this problem is to find the shortest path. It can be described as: a business traveler starts from a specific city, passes throughk cities, and only once, and finally returns to the starting point. It arranges city tours and forms hiking routes as soon as possible [17, 18]. The mathematical model of the ant colony algorithm is also based on this example.For a given TSP k-type city, the mathematical expression for the optimal path is expressed as:(3)COSTa1,…,ak=∑b=1k−1dEab,Eab+1+dEak,Eak+1.Among them,dEab,Eab+1 represents the distance between city a and city a + 1.Supposejat represents the number of ants in city a at time t, and the total number of ants in the entire model is constant:(4)b=∑jat.Δτabt is the pheromone intensity on edge (a, b).The probability of antl moving from one city to another is closely related to the concentration of pheromones in the path. In the mathematical model, the Gablt formula is used to express the random proportion rule, the probability of ant l moving from city a to city b at time t, corresponding to the next route:(5)Gabl=0τabεt⋅μabλt∑b∈allowedlτasεt⋅μasλt,b∈allowedl.Among them,allowedl represents the city that ant l is allowed to choose when exploring the next path.The parameter ɛ represents the heuristic information factor, it represents the relative importance of the information accumulated on the path by the ants in the process of finding the optimal path for the ants to choose the path. Andμ is the expected exponential factor. When an ant chooses a path, it indicates how much the ant attaches importance to the heuristic information in the process of finding the optimal path.The visibility of the route (a, b) is denoted as μabt, and it represents the heuristic degree of transport. And for the TSP problem, the expression is of the formula:(6)μabt=1dab.From the above formula,dab seems to represent the distance between two adjacent cities a and b. The smaller the dab distance, the larger the μabt distance.When the ants complete a cycle, the pheromone on each route must be changed according to the rules, which is the result of simulating a real ant colony [19]. The updated pheromone is calculated in such a way that a part of it evaporates naturally. At the same time, after passing new ants, a part will be left behind. The rules of the calculation method are as follows: at t + k, the amount of pheromone in the path (a, b) is adjusted according to the following formula:(7)τabt+k=1−χτabt=Δτabt,Δτabt=∑l=1oΔτablt.In the formula, 1 −χ is the attenuation factor of the pheromone, and the amplitude of χ is greater than or equal to zero and less than 1. The increase in the amount of pheromone along the path (a, b) in the loop is Δτabt. When Δτab0=0 is at time t = 0, the pheromone of ant l leaving a single ant l on the path (a, b) in this cycle is Δτablt.There are various models depending on the algorithm. The three most theoretically studied and analyzed (namely: Ant-Cycle of Ant-Cycle System, Am-Quantity of Ant-Quantity System and Ant-Density Model of Ant-Density System) are that they differ in the definition ofΔτablt rules.In the Ant-Cycle model:(8)Δτablt=PWlIf the ath ant passes througha,bin this cycle0otherwise.In formula (8), the total length of the lth ant passing through all cities in the circle is W, and P is a constant.In the Ant-Quantiy model:(9)Δτablt=PdabIf the ath ant passes througha,bin this cycle0otherwise.In the Ant-Density model:(10)Δτablt=PIf the ath ant passes througha,bin this cycle0otherwise.For these three models, the first mosquito-peripheral system, Ant-Cycle, uses global information to release pheromones after the ants have completed the entire route. While the other two models utilize local information to release pheromones simultaneously during the ant’s journey. Through experimental comparisons, the researchers found that the first model outperformed the other two models when it came to solving the TSP problem. It therefore usually takes it as the base model.


### 2.3. T-ACO Algorithm Based on Software Engineering

As an activity to ensure software quality, software testing has a specific design and sequence that requires a rigorous execution process. Since the idea of software testing was first proposed in the middle of the world, it has become an important part of the software engineering industry. Its theoretical research and technical application are constantly updated and improved, so that the industry still shines today.With the increasing emphasis on testing and the continuous deepening of testing research, more and more software development companies have begun to pay attention to software testing. It also uses automation technology to realize the purpose of the software test automation test tool. If the test efficiency and quality are to be improved, and the test cost should be further reduced, it is necessary to be able to correctly select and use automatic test tools [20]. Due to the high price of some widely used commercial automation testing software, several factors must be considered when choosing automation testing software. A logical and correct choice can only be made by taking into account the overall assessment.The basic ant colony algorithm is an artificial intelligence optimization algorithm that simulates the biological principles of ants. The basic ant colony algorithm determines the chosen path based on the number of remaining pheromones. The more pheromones left on the path, the better the route and the greater the chance of choice.The basic ant colony algorithm is easy to fall into the defects of local optimization and low optimization accuracy. To overcome the above shortcomings, T distribution is introduced in pheromone update. Population diversity increases due to the good disturbance effect of the T distribution. Therefore, without being constrained by the local optimum, the convergence speed of the algorithm can be improved and the probability of finding the global optimum solution can be improved.In order to improve the convergence speed, a limit can be set in advance to avoid invalid search due to poor solution after the ant moves once. At the same time, it is to avoid the ant colony algorithm falling into local minima. When adjusting the pheromone, T distribution is introduced to escape local minima. The modifier pheromones on each route apply to the following equation:(11)τabt+1=1−μτat+Δτb+Tt,Δτab=∑l=1mΔτbl,Δτbl=SCl0.The degree of pheromone volatility isμ, μ∈0,1; the sum of the pheromone concentrations of all ants between node a and node b is Δτab; the pheromone concentration Δτab is the pheromone released by the ants on the route between node a and node b. The total amount of pheromone released by the ant cycle is S; the size of S has an impact on the convergence speed of the algorithm; the length of the path traveled by the first ant is Cl; and T(T) is the T distribution variable.


## 2.1. Student’s T Distribution

The Gaussian operator has been used for a long time. It was first applied to the evolution strategy and gradually improved by other algorithms. In order to generate a wider range of variation, after that, some scholars replaced the Gaussian operator with the Cauchy operator and applied it to the optimization algorithm. It is undeniable that both operators have good mutation performance. Due to the large difference in variance between the two operators, the local search effect of Gaussian distribution is better and the global search effect of Cauchy distribution is better. For this reason, many scholars have thought of using the convolutional combination to obtain the average operator to balance the search ability, but after demonstrating that the average operator is equivalent to Cauchy in high-dimensional computing, it has no good practical effect. The emergence of Student’s t distribution can just synthesize the variation advantages of each division. Experiments have verified that this distribution has a good effect on balancing the mutation ability of global exploration and local fine search. Therefore, we use dynamic adaptive Student’s t-step to synthesize the variation effect [11, 12].The Student’s T distribution was proposed in 1908, mainly to solve the problem that the sample size is small and the population variance is unknown, which makes it impossible to use Gaussian distribution data to model [13]. It ushered in a new era of small sample statistics. m is the t distribution with parameter degrees of freedom, and the probability density G function is:(1)Gtz=Γm+1/2mπΓm/21+z2m−m+1/2,−∞<z<+∞,tm=1=D0,1 becomes a Cauchy distribution; if m≤2 has no variance; if m>2, the variance is m/m−2, tm⟶∞⟶M0,1. Therefore, two edge special cases of distributions are the Cauchy and Gaussian distributions, where t is 1 and t approaches infinity, respectively. In this regard, it is understood that choosing an appropriate degree of freedom m will take full advantage of the Gaussian and Cauchy distributions. The t-distribution variance also links Gaussian and Cauchy variables for a better mutation operator [14], as shown in Figure 1.Figure 1
t-distribution plot for different degrees of freedom.


## 2.2. Ant Colony Algorithm

The foraging behavior of ants is a cooperative process. On the way from the nest to the food source, ants secrete a substance, which other ants depend on, called pheromone [15, 16]. In the process of finding a path, ants can find the presence and strength of this substance and use it to judge the chosen path. Under natural conditions, pheromones also evaporate at a certain rate. The foraging behavior of ants is a self-organizing behavior, which can quickly find the shortest food path through cooperation. Figures 2–5 show the process of ants looking for food in reality.Figure 2
There are no obstacles between the nest and the food source.Figure 3
Place barriers between ant nests and food sources.Figure 4
Ants bypass obstacles to find food.Figure 5
Ants choose short paths.Assuming that there arez ants in total, Xz, Yz represent the number of ants passing on the X bridge and the Y bridge, respectively (as shown in Figure 6). Then whether the z + 1 ant will choose the X bridge or the Y bridge, we need to use the probability method to express this possibility. If we use Gx to represent the probability of choosing the X bridge, and Gy to represent the probability of choosing the N bridge, then:(2)Gxz=Xz+ldXz+ld+Yz+ld,Gyz=1−Gxz.Figure 6
Asymmetric double bridge experiment.In the formula,l represents the probability of random selection, and d represents the influence coefficient of the released pheromone concentration on the ants’ path selection. If Gx>Gy, then the probability of the z + 1th ant choosing X bridge is relatively high. If Gx<Gy, then the probability of the z + 1th ant to choose the Y bridge is relatively high.Before introducing the ant system model, let us look at the traveling salesman problem. The core problem of this problem is to find the shortest path. It can be described as: a business traveler starts from a specific city, passes throughk cities, and only once, and finally returns to the starting point. It arranges city tours and forms hiking routes as soon as possible [17, 18]. The mathematical model of the ant colony algorithm is also based on this example.For a given TSP k-type city, the mathematical expression for the optimal path is expressed as:(3)COSTa1,…,ak=∑b=1k−1dEab,Eab+1+dEak,Eak+1.Among them,dEab,Eab+1 represents the distance between city a and city a + 1.Supposejat represents the number of ants in city a at time t, and the total number of ants in the entire model is constant:(4)b=∑jat.Δτabt is the pheromone intensity on edge (a, b).The probability of antl moving from one city to another is closely related to the concentration of pheromones in the path. In the mathematical model, the Gablt formula is used to express the random proportion rule, the probability of ant l moving from city a to city b at time t, corresponding to the next route:(5)Gabl=0τabεt⋅μabλt∑b∈allowedlτasεt⋅μasλt,b∈allowedl.Among them,allowedl represents the city that ant l is allowed to choose when exploring the next path.The parameter ɛ represents the heuristic information factor, it represents the relative importance of the information accumulated on the path by the ants in the process of finding the optimal path for the ants to choose the path. Andμ is the expected exponential factor. When an ant chooses a path, it indicates how much the ant attaches importance to the heuristic information in the process of finding the optimal path.The visibility of the route (a, b) is denoted as μabt, and it represents the heuristic degree of transport. And for the TSP problem, the expression is of the formula:(6)μabt=1dab.From the above formula,dab seems to represent the distance between two adjacent cities a and b. The smaller the dab distance, the larger the μabt distance.When the ants complete a cycle, the pheromone on each route must be changed according to the rules, which is the result of simulating a real ant colony [19]. The updated pheromone is calculated in such a way that a part of it evaporates naturally. At the same time, after passing new ants, a part will be left behind. The rules of the calculation method are as follows: at t + k, the amount of pheromone in the path (a, b) is adjusted according to the following formula:(7)τabt+k=1−χτabt=Δτabt,Δτabt=∑l=1oΔτablt.In the formula, 1 −χ is the attenuation factor of the pheromone, and the amplitude of χ is greater than or equal to zero and less than 1. The increase in the amount of pheromone along the path (a, b) in the loop is Δτabt. When Δτab0=0 is at time t = 0, the pheromone of ant l leaving a single ant l on the path (a, b) in this cycle is Δτablt.There are various models depending on the algorithm. The three most theoretically studied and analyzed (namely: Ant-Cycle of Ant-Cycle System, Am-Quantity of Ant-Quantity System and Ant-Density Model of Ant-Density System) are that they differ in the definition ofΔτablt rules.In the Ant-Cycle model:(8)Δτablt=PWlIf the ath ant passes througha,bin this cycle0otherwise.In formula (8), the total length of the lth ant passing through all cities in the circle is W, and P is a constant.In the Ant-Quantiy model:(9)Δτablt=PdabIf the ath ant passes througha,bin this cycle0otherwise.In the Ant-Density model:(10)Δτablt=PIf the ath ant passes througha,bin this cycle0otherwise.For these three models, the first mosquito-peripheral system, Ant-Cycle, uses global information to release pheromones after the ants have completed the entire route. While the other two models utilize local information to release pheromones simultaneously during the ant’s journey. Through experimental comparisons, the researchers found that the first model outperformed the other two models when it came to solving the TSP problem. It therefore usually takes it as the base model.


## 2.3. T-ACO Algorithm Based on Software Engineering

As an activity to ensure software quality, software testing has a specific design and sequence that requires a rigorous execution process. Since the idea of software testing was first proposed in the middle of the world, it has become an important part of the software engineering industry. Its theoretical research and technical application are constantly updated and improved, so that the industry still shines today.With the increasing emphasis on testing and the continuous deepening of testing research, more and more software development companies have begun to pay attention to software testing. It also uses automation technology to realize the purpose of the software test automation test tool. If the test efficiency and quality are to be improved, and the test cost should be further reduced, it is necessary to be able to correctly select and use automatic test tools [20]. Due to the high price of some widely used commercial automation testing software, several factors must be considered when choosing automation testing software. A logical and correct choice can only be made by taking into account the overall assessment.The basic ant colony algorithm is an artificial intelligence optimization algorithm that simulates the biological principles of ants. The basic ant colony algorithm determines the chosen path based on the number of remaining pheromones. The more pheromones left on the path, the better the route and the greater the chance of choice.The basic ant colony algorithm is easy to fall into the defects of local optimization and low optimization accuracy. To overcome the above shortcomings, T distribution is introduced in pheromone update. Population diversity increases due to the good disturbance effect of the T distribution. Therefore, without being constrained by the local optimum, the convergence speed of the algorithm can be improved and the probability of finding the global optimum solution can be improved.In order to improve the convergence speed, a limit can be set in advance to avoid invalid search due to poor solution after the ant moves once. At the same time, it is to avoid the ant colony algorithm falling into local minima. When adjusting the pheromone, T distribution is introduced to escape local minima. The modifier pheromones on each route apply to the following equation:(11)τabt+1=1−μτat+Δτb+Tt,Δτab=∑l=1mΔτbl,Δτbl=SCl0.The degree of pheromone volatility isμ, μ∈0,1; the sum of the pheromone concentrations of all ants between node a and node b is Δτab; the pheromone concentration Δτab is the pheromone released by the ants on the route between node a and node b. The total amount of pheromone released by the ant cycle is S; the size of S has an impact on the convergence speed of the algorithm; the length of the path traveled by the first ant is Cl; and T(T) is the T distribution variable.


## 3. Software Testing Technology Experiment

Software testing is a systematic project with a certain complexity. Its main purpose is to find various possible design flaws and execution errors in software, and on this basis, spend as little time and manpower as possible. Its production and progress undoubtedly greatly promoted the maturity of software testing theory. In order to expose as many problems in the code as possible, it is necessary to use test cases to check the program. Test cases must be designed according to documents (such as requirements and drawing documents) at various stages of software development, or directly refer to the internal structure of the program to be tested. As an emerging industry, software testing is gradually transitioning from manual testing to automated testing to create more accurate and faster software testing systems. In order to adapt to this change, it is necessary to introduce higher requirements on how to create test cases, therefore, automated software test production technology came into being.


### 3.1. Key Technologies of Software Engineering

Due to the importance and complexity of software testing, it takes almost half or more of the time in the entire software development cycle. As a complex task, testing will take a lot of time and manpower. How to efficiently and automatically create tests plays a key role in software test development. This is because automatic test creation techniques do not require manual test design, which not only eliminates the need for auditors to spend a lot of time designing test cases. It also reduces the workload of the software controller, improves the efficiency, and eliminates the subjective factor of the software controller. It makes the test more objective, accurate, and convincing. The key technologies of software engineering include software service engineering, crowdsourcing software service engineering, intensive data research, and computer information processing technology (as shown in Figure7).Figure 7
Key technologies of software engineering.The application of software service technology can protect the system and software in the local network, which is mainly reflected in the development and configuration of business application software. The application software on the local network is placed in the overall security protection and management system to effectively prevent external virus intrusion. In addition, the current software service engineering also reflects the customization function in the application, which can fully meet the changing needs of users.Crowdsourced software service engineering is different from software service engineering. They all have different service items. The service objects of crowdsourcing software engineering services include not only the operation management platform, but also other management platforms. At the same time, crowdsourced software service engineering can realize data sharing according to the instruction introduction and improve the effectiveness of each implementation process. In the process of crowdsourcing software service, the basis of its analysis is the professional theory used by various disciplines, and the results of data analysis have a high reference value. However, due to the variability of the external world, the analysis results cannot be fully replicated.Data-intensive research should create a unified research theory and methodology that emphasizes the importance of big data. It uses the fourth example to study dense data, determine method and structure, and analyze key issues. Intensive data research differs from traditional methods and requires a change in the way people think. It creates a scientifically complete fourth example. A complete and unified theoretical system is created, which is then gradually transformed into a third example.Under the existing background, it is difficult to obtain timely and effective processing of computer information processing technology. Networks created by hardware capabilities have limitations that limit the evolution of network performance. It is necessary to innovatively explore computer network architecture technology and use the network to process big data. A computer network must first have an open transmission function and network structure so that the ability to process information does not depend on computer hardware. It defines the network structure and avoids the use of network software to develop network technology.


### 3.2. Automatic Data Generation of T-ACO Algorithm in Software Engineering

In the field of automatic creation of software test data, the triangular classification problem is an eternal and classic problem. Many researchers use it as a benchmark program for the path test problem. The triangle classification program seems simple, but the logical structure contained in it is relatively complex. Therefore, this procedure is widely used in experiments for automatically generating test data. This chapter also uses the program of triangle classification as the test program to verify the performance of various algorithms.The problem of a triangle classification program can be simply described as the input to the program is the three sides of the triangle and the output is the type of the triangle. The test data are randomly generated. If the data to be generated happens to cover the path of an equilateral triangle, the probability of this randomness is very small. That is, it is more difficult to cover the branches of an equilateral triangle.The algorithm in this paper (T-ACO) is compared with the basic ant colony algorithm (ACO) and genetic algorithm (GA). The experiments were compared and analyzed from the number of iterations (NI), the average running time (Average running time, ART), and the success rate (Success rate, SR). It compares the advantages and disadvantages of the algorithm in this paper. Experimental setup: This paper considers the effects of the data range (Datarange, DR), population size (Population size, PS), and maximum iterations (Maximum algebra, MA) on test case generation.First, an experiment is performed using a triangle classification procedure. The range of input data is divided into four cases, and in different cases, different population sizes and maximum termination generations are selected. The experimental setup is shown in Table1, and the results are shown in Figure 8. Among them, the success rate refers to the ratio of the number of experiments that successfully generated test data to the total number of experiments within the maximum number of algebras.Table 1
Experimental results of the triangle classification procedure.
Experiment settingDRPSMA1[1084]40802[1196]401603[1482]804004[10066]160500Figure 8
Experimental results of the triangle classification procedure. (a) Comparison of the number of iterations of the three algorithms under four different experimental settings. (b) Comparison of the average running time of the three algorithms under four different experimental settings. (c) Comparison of the success rates of the three algorithms under four different experimental settings.
(a)(b)(c)As shown in Figure8, the algorithm in this paper successfully generates test data with the least number of repetitions for each data range. The algorithm in this paper significantly outperforms the other two algorithms in terms of average running time.A larger input range of the triangle classification program is selected for experiments. The experimental settings are shown in Table2, and the results are shown in Figure 9:Table 2
Triangle classifier experimental setup under large search space.
Experiment settingDRPSMA1[1492]18010002[10688]18020003[11626]18030004[13644]18040005[17792]1805000Figure 9
Experimental results of triangle classification procedure under large search space. (a) Comparison of the number of iterations. (b) Average running time comparison. (c) Comparison of success rates.
(a)(b)(c)As can be seen from Figure9, when the data range becomes larger and the search space grows explosively, it is difficult for basic ACO and GA to generate test data for small probability situations. Although the algorithm in this paper increases the number of iterations and the running time is longer with the increase of the data range, it can still generate test data effectively. Overall, for both reference procedures, the method has clear advantages in terms of the success rate of test data generation, number of iterations, and execution time compared to basic ACO and GA. To further verify the validity, this paper selects 6 industrial use cases for experiments. In this set of experiments, each method was determined to be performed 200 times independently. The results are shown in Table 3 and Figure 10.Table 3
Average runtime for industrial use cases.
ART (s)T-ACOACOGA1Space (fixgramp)0.02880.03330.03562Space (ixsgrid)0.03010.03910.03973Tot_info8.84198.010225.60454Replace16.508941.879428.89455Sed29.809470.039742.48646Flex37.4310103.054960.9885Figure 10
Industrial use case experimental results. (a) Comparison of iteration times for industrial use cases. (b) Comparison of success rates of industrial use cases.
(a)(b)As shown in Table3 and Figure 10, the quantity of cycles in this strategy is essentially not exactly that of fundamental ACO and GA. In terms of running time, our method is shorter than the other two methods. The exploratory outcomes show that contrasted and the fundamental ACO and GA, the technique in this paper enjoys clear benefits in both the quantity of emphasis and the running time. It is additionally more productive in creating test information.Each experiment was run 50 times, and Table4 shows the comparison of the total path coverage between the T-ACO algorithm and the comparison algorithm.Table 4
Comparison of coverage ratio of each algorithm (%).
Population sizeT-ACOACOGABestMeanBestMeanBestMean15100.0099.20100.0092.00100.0092.2030100.0099.40100.0093.00100.0092.2035100.00100.00100.0093.80100.0094.3060100.00100.00100.0094.30100.0094.0075100.00100.00100.0094.90100.0095.0090100.00100.00100.0095.60100.0095.80105100.00100.00100.0096.50100.0096.60120100.00100.00100.0095.00100.0096.10It can be found from Table4 that the optimal test case sets output by all experimental algorithms can achieve the coverage of the total path, but there are differences in the average coverage data set. The results show that the T-ACO algorithm has certain advantages in the quality of test case set generation. The comparison of the average number of iterations of each algorithm is shown in Figure 11(a), and the average execution time is shown in Figure 11(b).Figure 11
Algorithm average iteration number graph and execution time comparison graph. (a) Comparison of the average number of iterations of the algorithm. (b) Algorithm execution time comparison graph.
(a)(b)The average number of iterations of the algorithm can be visually compared in Figure11(a). It can be seen that the average number of iterations of the comparison algorithm is about 70 times, while the T-ACO algorithm is about 20 times, which is better than the comparison algorithm and shows better use case generation performance.It can be seen from Figure11(b) that the T-ACO algorithm has certain advantages in average execution time. With the expansion of the population size, the average execution time of both the T-ACO algorithm and the comparison algorithm increases. The execution time of the T-ACO algorithm is shorter than that of other comparison algorithms under all experimental population sizes. And with the expansion of the population size, the advantage of execution time becomes more and more obvious.


## 3.1. Key Technologies of Software Engineering

Due to the importance and complexity of software testing, it takes almost half or more of the time in the entire software development cycle. As a complex task, testing will take a lot of time and manpower. How to efficiently and automatically create tests plays a key role in software test development. This is because automatic test creation techniques do not require manual test design, which not only eliminates the need for auditors to spend a lot of time designing test cases. It also reduces the workload of the software controller, improves the efficiency, and eliminates the subjective factor of the software controller. It makes the test more objective, accurate, and convincing. The key technologies of software engineering include software service engineering, crowdsourcing software service engineering, intensive data research, and computer information processing technology (as shown in Figure7).Figure 7
Key technologies of software engineering.The application of software service technology can protect the system and software in the local network, which is mainly reflected in the development and configuration of business application software. The application software on the local network is placed in the overall security protection and management system to effectively prevent external virus intrusion. In addition, the current software service engineering also reflects the customization function in the application, which can fully meet the changing needs of users.Crowdsourced software service engineering is different from software service engineering. They all have different service items. The service objects of crowdsourcing software engineering services include not only the operation management platform, but also other management platforms. At the same time, crowdsourced software service engineering can realize data sharing according to the instruction introduction and improve the effectiveness of each implementation process. In the process of crowdsourcing software service, the basis of its analysis is the professional theory used by various disciplines, and the results of data analysis have a high reference value. However, due to the variability of the external world, the analysis results cannot be fully replicated.Data-intensive research should create a unified research theory and methodology that emphasizes the importance of big data. It uses the fourth example to study dense data, determine method and structure, and analyze key issues. Intensive data research differs from traditional methods and requires a change in the way people think. It creates a scientifically complete fourth example. A complete and unified theoretical system is created, which is then gradually transformed into a third example.Under the existing background, it is difficult to obtain timely and effective processing of computer information processing technology. Networks created by hardware capabilities have limitations that limit the evolution of network performance. It is necessary to innovatively explore computer network architecture technology and use the network to process big data. A computer network must first have an open transmission function and network structure so that the ability to process information does not depend on computer hardware. It defines the network structure and avoids the use of network software to develop network technology.


## 3.2. Automatic Data Generation of T-ACO Algorithm in Software Engineering

In the field of automatic creation of software test data, the triangular classification problem is an eternal and classic problem. Many researchers use it as a benchmark program for the path test problem. The triangle classification program seems simple, but the logical structure contained in it is relatively complex. Therefore, this procedure is widely used in experiments for automatically generating test data. This chapter also uses the program of triangle classification as the test program to verify the performance of various algorithms.The problem of a triangle classification program can be simply described as the input to the program is the three sides of the triangle and the output is the type of the triangle. The test data are randomly generated. If the data to be generated happens to cover the path of an equilateral triangle, the probability of this randomness is very small. That is, it is more difficult to cover the branches of an equilateral triangle.The algorithm in this paper (T-ACO) is compared with the basic ant colony algorithm (ACO) and genetic algorithm (GA). The experiments were compared and analyzed from the number of iterations (NI), the average running time (Average running time, ART), and the success rate (Success rate, SR). It compares the advantages and disadvantages of the algorithm in this paper. Experimental setup: This paper considers the effects of the data range (Datarange, DR), population size (Population size, PS), and maximum iterations (Maximum algebra, MA) on test case generation.First, an experiment is performed using a triangle classification procedure. The range of input data is divided into four cases, and in different cases, different population sizes and maximum termination generations are selected. The experimental setup is shown in Table1, and the results are shown in Figure 8. Among them, the success rate refers to the ratio of the number of experiments that successfully generated test data to the total number of experiments within the maximum number of algebras.Table 1
Experimental results of the triangle classification procedure.
Experiment settingDRPSMA1[1084]40802[1196]401603[1482]804004[10066]160500Figure 8
Experimental results of the triangle classification procedure. (a) Comparison of the number of iterations of the three algorithms under four different experimental settings. (b) Comparison of the average running time of the three algorithms under four different experimental settings. (c) Comparison of the success rates of the three algorithms under four different experimental settings.
(a)(b)(c)As shown in Figure8, the algorithm in this paper successfully generates test data with the least number of repetitions for each data range. The algorithm in this paper significantly outperforms the other two algorithms in terms of average running time.A larger input range of the triangle classification program is selected for experiments. The experimental settings are shown in Table2, and the results are shown in Figure 9:Table 2
Triangle classifier experimental setup under large search space.
Experiment settingDRPSMA1[1492]18010002[10688]18020003[11626]18030004[13644]18040005[17792]1805000Figure 9
Experimental results of triangle classification procedure under large search space. (a) Comparison of the number of iterations. (b) Average running time comparison. (c) Comparison of success rates.
(a)(b)(c)As can be seen from Figure9, when the data range becomes larger and the search space grows explosively, it is difficult for basic ACO and GA to generate test data for small probability situations. Although the algorithm in this paper increases the number of iterations and the running time is longer with the increase of the data range, it can still generate test data effectively. Overall, for both reference procedures, the method has clear advantages in terms of the success rate of test data generation, number of iterations, and execution time compared to basic ACO and GA. To further verify the validity, this paper selects 6 industrial use cases for experiments. In this set of experiments, each method was determined to be performed 200 times independently. The results are shown in Table 3 and Figure 10.Table 3
Average runtime for industrial use cases.
ART (s)T-ACOACOGA1Space (fixgramp)0.02880.03330.03562Space (ixsgrid)0.03010.03910.03973Tot_info8.84198.010225.60454Replace16.508941.879428.89455Sed29.809470.039742.48646Flex37.4310103.054960.9885Figure 10
Industrial use case experimental results. (a) Comparison of iteration times for industrial use cases. (b) Comparison of success rates of industrial use cases.
(a)(b)As shown in Table3 and Figure 10, the quantity of cycles in this strategy is essentially not exactly that of fundamental ACO and GA. In terms of running time, our method is shorter than the other two methods. The exploratory outcomes show that contrasted and the fundamental ACO and GA, the technique in this paper enjoys clear benefits in both the quantity of emphasis and the running time. It is additionally more productive in creating test information.Each experiment was run 50 times, and Table4 shows the comparison of the total path coverage between the T-ACO algorithm and the comparison algorithm.Table 4
Comparison of coverage ratio of each algorithm (%).
Population sizeT-ACOACOGABestMeanBestMeanBestMean15100.0099.20100.0092.00100.0092.2030100.0099.40100.0093.00100.0092.2035100.00100.00100.0093.80100.0094.3060100.00100.00100.0094.30100.0094.0075100.00100.00100.0094.90100.0095.0090100.00100.00100.0095.60100.0095.80105100.00100.00100.0096.50100.0096.60120100.00100.00100.0095.00100.0096.10It can be found from Table4 that the optimal test case sets output by all experimental algorithms can achieve the coverage of the total path, but there are differences in the average coverage data set. The results show that the T-ACO algorithm has certain advantages in the quality of test case set generation. The comparison of the average number of iterations of each algorithm is shown in Figure 11(a), and the average execution time is shown in Figure 11(b).Figure 11
Algorithm average iteration number graph and execution time comparison graph. (a) Comparison of the average number of iterations of the algorithm. (b) Algorithm execution time comparison graph.
(a)(b)The average number of iterations of the algorithm can be visually compared in Figure11(a). It can be seen that the average number of iterations of the comparison algorithm is about 70 times, while the T-ACO algorithm is about 20 times, which is better than the comparison algorithm and shows better use case generation performance.It can be seen from Figure11(b) that the T-ACO algorithm has certain advantages in average execution time. With the expansion of the population size, the average execution time of both the T-ACO algorithm and the comparison algorithm increases. The execution time of the T-ACO algorithm is shorter than that of other comparison algorithms under all experimental population sizes. And with the expansion of the population size, the advantage of execution time becomes more and more obvious.


## 4. Conclusion

With the rapid development of the software engineering industry, the software has been widely used in many different fields. Likewise, the requirements for software quality are becoming more and more stringent. In order to meet the requirements of current software in terms of safety and reliability, people put forward high requirements for software quality, which also gave birth to the disciplined software testing system. How to effectively carry out software testing has become the common research direction of many experts and scholars in the world. It is getting more and more attention from software professionals. The cost of testing can be high in some key industries and technology sectors, which is one of the reasons why software testing is getting a lot of attention. Automating the creation of tests can undoubtedly significantly reduce the workload of the relevant auditors. It eliminates human interference to a certain extent and improves the effectiveness and accuracy of detection. Therefore, one of the key factors to improve the effectiveness and accuracy of software testing is the automatic creation method of test cases. This paper gives an overview of the student’s t distribution algorithm and then introduces the basic principles and characteristics of the ant colony algorithm in detail. Finally, this paper combines the t distribution with the parallel nature of the ant colony algorithm itself. After the improvement, this paper introduces ant colony strategies such as migration rules and pheromone update and proposes the T-ACO algorithm to create test cases. The advantages of this method and other algorithms are verified by experiments.



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