This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

Context Software effort estimation (SEE) is most crucial activity in the field of software engineering. Vast research has been conducted in SEE resulting into a tremendous increase in literature. Thus it is of utmost importance to identify the core research areas and trends in SEE which may lead the researchers to understand and discern the research patterns in large literature dataset. Objective To identify unobserved research patterns through natural language processing from a large set of research articles on SEE published during the period 1996 to 2016. Method A generative statistical method, called Latent Dirichlet Allocation (LDA), applied on a literature dataset of 1178 articles published on SEE. Results As many as twelve core research areas and sixty research trends have been revealed; and the identified research trends have been semantically mapped to associate core research areas. Conclusions This study summarises the research trends in SEE based upon a corpus of 1178 articles. The patterns and trends identified through this research can help in finding the potential research areas.

Research patterns and trends in software effort estimation

Advancement from neural networks to deep learning in software effort estimation: Perspective of two decades

In this paper, software effort prediction (SEP) and software test prediction (STP) (i.e., software reliability problems) are tackled by integrating the salp swarm algorithm (SSA) with a backpropagation neural network (BPNN). Software effort and test prediction problems are common in software engineering and arise when seeking to determine the actual software resources needed to develop a project. BPNN is the most popular prediction algorithm used in the literature. The performance of BPNN depends totally on the initial parameter values such as weight and biases. The main objective of this paper is to integrate SSA with the BPNN to find the optimal weight for every training cycle and thereby improve prediction accuracy. The proposed method, abbreviated as SSA-BPNN, is tested on twelve SEP datasets and two STP datasets. All datasets vary in terms of complexity and size. The results obtained by SSA-BPNN are evaluated according to twelve performance measures: MSE, RMSE, RAE, RRSE, MAE, MRE, MMRE, MdMRE, VAF(%), R2(%), ED, and MD. First, the results obtained by BPNN with SSA (i.e., SSA-BPNN) and without SSA are compared. The evaluation of the results indicates that SSA-BPNN performs better than BPNN for all datasets. In the comparative evaluation, the results of SSA-BPNN are compared against thirteen state-of-the-art methods using the same SEP and STP problem datasets. The evaluation of the results reveals that the proposed method outperforms the comparative methods for almost all datasets, both SEP and STP, in the case of most performance measures. In conclusion, integrating SSA with BPNN is a very powerful approach for solving software reliability problems that can be used widely to yield accurate prediction results.

Salp Swarm Optimizer for Modeling Software Reliability Prediction Problems

Software Defect Estimation (SDE) is a fundamental problem solving mechanism in the field of software engineering (SE). SDE is a task that identifies software models that are likely to have defects. In addition, SDE plays a vital overall role in improving software quality, reducing software development costs and accelerating software development processes. The Backpropagation Neural Network (BPNN) is a popular machine learning (ML) estimator widely utilized in SE estimation problems. Unfortunately, its performance depends on the initial weight and bias values. Metaheuristic optimization algorithms, as an alternative method, have proven to have strengths in parameter optimizations. Additionally, population-based metaheuristic algorithms suffer from low exploitation capabilities. In this paper, a new hybrid metaheuristic algorithm-based BPNN (SSA–SA) is proposed by hybridizing the Salp Swarm Algorithm (SSA) with the Simulated Annealing (SA) algorithm. The main goal of the hybridization is to adjust the balance between exploration and exploitation in SSA. The proposed algorithm is also assembled with the BPNN estimator to optimize its parameters to reduce the overall estimation error, which boosts the estimation accuracy. Thus, the proposed algorithm addresses the SDE problem. Experimental results prove the superiority of the proposed hybrid algorithm in optimizing BPNN parameters in comparisons against other estimators and algorithms in most SDE datasets and evaluation criteria. 

Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm

Correct cost estimates is an essential element to develop economic proposals and being competitive in the market. Companies have to estimate the effort, time and cost before bidding for a project. An inaccurate estimate will lead to money and market share loss. To do that the expected software development size has to be estimated early in the development process. Many software models were presented in the literature to handle this task such as expert judgment, analogy-based estimation, formal estimation models and combination-based estimation models. These models were found to be risky and created many problems in practice related to availability of expertise and inaccurate estimate. Soft Computing techniques were successfully used to solve a diversity of problems in software engineering project cost estimation management. Earlier investigation proved that techniques such as Artificial Neural Networks (ANNs) can solve many problems in the field of software engineering project cost estimation management with promising results. In this paper, we propose two new models for software effort and function point estimation using ANNs. Two types of ANNs will be explored; the Feedforward (FF) and the Radial Basic Function (RBF) neural networks. The Albrecht data set with a number of attributes was used to provide our results. Developed results shows that ANNs models can provide an accurate estimate for both the software effort and number of function points.

Software Effort and Function Points Estimation Models Based Radial Basis Function and Feedforward Artificial Neural Networks

On time and within budget software project development represents a challenge for software project managers. Software management activities include but are not limited to: estimation of project cost, development of schedules and budgets, meeting user requirements and complying with standards. Recruiting development team members is a sophisticated problem for a software project manager. Since the utmost cost in software development effort is manpower, software project effort and is associated cost estimation models are used in estimating the effort required to complete a project. This effort estimate can then be converted into dollars based on the proper labor rates. An initial development team needs to be selected not only at the beginning of the project but also during the development process. It is important to allocate the necessary team to a project and efficiently distribute their effort during the development life cycle. In this paper, we provide our initial idea of developing a prediction model for defining the estimated required number of test workers of a software project during the software testing process. The developed models utilize the test instance and the number of observed faults as input to the proposed models. Artificial Neural Networks (ANNs) successfully build the dynamic relationships between the inputs and output and produce and accurate predication estimates.

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Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

In this paper, an enhanced version of the salp swarm algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) Diversification of the SSA population referred as SSA $$_{std}$$ , (ii) SSA parameters are tuned using a self-adaptive technique-based genetic algorithm (GA) referred as SSA $$_{GA-tuner}$$ . The novelty of developing a self-adaptive SSA is to enhance its performance through balancing search exploration and exploitation. The enhanced SSA versions are evaluated using twelve benchmark functions. The diversified population of SSA $$_{std}$$ enhances convergence behavior, and self-adaptive parameter tuning of SSA $$_{GA-tuner}$$ improves the convergence behavior as well, thus improving performance. The comparative evaluation against nine well-established methods shows the superiority of the proposed SSA versions. The enhancement amount in accuracy was between 2.97 and 99% among all versions of algorithm. In a nutshell, the proposed SSA version shows a powerful enhancement that can be applied to a wide range of optimization problems. 

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Self-adaptive salp swarm algorithm for optimization problems

The power schedule problem (PSP) is the problem of managing, controlling, and scheduling power consumption of electrical appliances/devices to operate at the best periods according to several constraints and objectives. The PSP is a complex and high-constraint scheduling problem, making its search space extensive and rugged. The PSP components can be controlled and managed by utilizing a communication approach that interconnects the appliances and enhances exchanging data. Several communication approaches were used for the PSP, where the Internet of Things (IoT) is the best for data exchange. The PSP has been extensively handled using various optimization approaches, particularly metaheuristics, due to their capabilities to optimize different search space scales. Nevertheless, in some cases, these optimization algorithms suffer from low execution abilities, especially with huge search spaces like the PSP. In this study, a recent metaheuristic, called white shark optimizer (WSO), is adapted and enhanced to address the PSP efficiently. The proposed enhanced method is introduced to improve the WSO optimization performance and find better schedules for the PSP by hybridizing the WSO components with a well-known optimization algorithm called equilibrium optimizer. The proposed method is called the white shark equilibrium optimizer (WSEO). The proposed method is operated through a residential IoT system to manage home appliances efficiently. Moreover. the PSP is mathematically formulated as multi-objective PSP considering three main objectives, including electricity bills, power consumption balance, and users’ comfortabilities. In the evaluation stage, a new case study in the United Arab Emirates (UAE) is proposed that contains most of the available appliances in the UAE. The evaluation is presented in three main phases, including original, original with a hybrid approach, and hybrid approach evaluations. The proposed WSEO outperformed all compared methods in optimizing the PSP.

Sofian Kassaymeh

Papers

Backpropagation Neural Network optimization and software defect estimation modelling using a hybrid Salp Swarm optimizer-based Simulated Annealing Algorithm

Software Effort and Function Points Estimation Models Based Radial Basis Function and Feedforward Artificial Neural Networks

Estimating the Number of Test Workers Necessary for a Software Testing Process Using Artificial Neural Networks

Self-adaptive salp swarm algorithm for optimization problems

A Hybrid White Shark Equilibrium Optimizer for Power Scheduling Problem Based IoT