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

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

Accurate effort estimation is amongst the key activities in the software project development. It directly impacts the time and cost of the software projects. This paper presents a systematic literature review of software effort estimation techniques using machine learning. This review presents a discussion about the research trends in machine learning inspired software effort estimation. The results of the systematic review has concluded prominent trends of machine learning approaches, size metrics, benchmark datasets, validation methods etc. used for software effort estimation.

Systematic Literature Review on Software Effort Estimation Using Machine Learning Approaches

One of all the works on process mining is the process discovery which produces a representation of a parallel business process. This representation is called process model and it  consists of sequence and parallel control-flow patterns. The parallel control-flow patterns contain XOR, AND, and OR relations. Hidden Markov Model is rarely used to represent a process model since XOR, AND and OR relations are not visible. In Hidden Markov Model, the control-flow patterns are represented by probabilities of state transitions. This research proposes an algorithm consisting in a process discovery based on Hidden Markov Model. This algorithm contains equations and rules: the equations are used to differentiate XOR, AND, and OR relations, while the rules are used to establish the process model utilizing detected control-flow patterns. The experiment results show that the proposed algorithm obtain the right control-flow patterns in the process model. The paper demonstrates that the fitness of process models obtained by the proposed algorithm are relatively higher respect to those obtained by Heuristics Miner and Time-based Heuristics Miner algorithms. This paper also shows that the validity of process models obtained by the proposed algorithm are better than those obtained by other algorithms.

Hidden Markov Model for Process Mining of Parallel Business Processes

Jambi Province is a producer of palm oil as a mainstay of commodities. However, the limited insight of farmers in Jambi to oil palm pests and diseases affects oil palm productivity. Meanwhile, knowing the types of pests and diseases in oil palm requires an expert, but access restrictions are a problem. This study offers a diagnosis of oil palm disease using the most popular concept in the field of artificial intelligence today. This method is deep learning. Various recent studies using CNN, say the results of image recognition accuracy are very good. The data used in this study came from oil palm image data from the Jambi Provincial Plantation Office. After the oil palm disease image data is trained, the training data model will be stored for the process of testing the oil palm disease diagnosis. The test evaluation is stored as a configuration matrix. So that it can be assessed how successful the system is to diagnose diseases in oil palm plants. From the testing, there were 2490 images of oil palm labeled with 11 disease categories. The highest accuracy results were 0.89 and the lowest was 0.83, and the average accuracy was 0.87. This shows that the results of the classification of oil palm images with CNN are quite good. These results can indicate the development of an automatic and mobile oil palm disease classification system to help farmers.

/pdf/analisis-dan-implementasi-diagnosis-penyakit-sawit-dengan-1l5k59oa15.pdf

Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN)

Nowadays, Business Process Management (BPM) technology has been developed and widely applied in small and large scale. There are many companies and organizations implemented business process oriented information systems. Therefore, the flexibility issues become very important in managing large quantity of business processes because business processes are always changing overtime. There is a need of flexible business process models so that every possible changes can be done easily every time. Flexible business process models are represented by a reconfigurable model. In this paper, we proposed a method to manage business process model variations in order to efficiently develop the business process repository. The variation management is done by storing the common business processes and its variation in the repository. Then, we also propose meta models which contain information about the models stored in the repository. The results show that the meta models support the composition process of a new business process based on the common business process. The variation management has achieved to reduce storage redundancy up to 82%.

Business process composition based on meta models

This research will observe the use of Artificial Neural Networks (ANN) for estimating software cost. Constructive Cost Model (COCOMO) is the most famous estimating model for software cost, which will be used in this research. The model estimates software cost by calculating several variables which are created by expert with some equations. Furthermore, ANN helps to estimate COCOMO effort accurately. This research offers multilayer feed-forward neural network to adjust COCOMO effort estimation parameters. Also, an algorithm such as Back-propagation is applied to improve the architecture by comparing actual effort with estimated effort and updating the network. However, there are several types of neural network architecture. This research tries to compare several types of architecture by testing each architecture model to dataset. This paper concerns with two different architectures. The difference of this two architecture is basic architecture only uses effort multipliers as input layer while modified architecture divides input layer into two categories such as effort multipliers and scale factors. The result is the proposed model increases the accuracy and each model has different result.

Comparison of different Neural Network architectures for software cost estimation

Constructive Cost Model II (COCOMO II) is investigated as the most popular model for software cost estimation. COCOMO II depends on several variables or Cost Drivers (CD). This research investigates the role of Effort Multiplier (EM) and Line of Code (LOC) to improve the accuracy of cost estimation. Fuzzy Logic has been implemented to the COCOMO II to represent the EM. Furthermore, in order to produce better estimation, this research uses Gaussian Membership Function to redesign the Effort Multiplier by studying the behavior of COCOMO II. This research also applies Neural Network (NN) approach to increase the accuracy of software effort estimation by training the software development datasets. The result is proposed model gives contribution to decrease error significantly.

Improving the accuracy of COCOMO's effort estimation based on neural networks and fuzzy logic model

Software Cost Estimation is a problem that often appears on the project of making a software. A poor estimate will result in a bad project management. To resolve this problem, a lot of the estimation model was introduced. Constructive Cost Model II (COCOMO II) is the most known and widely used model for software cost estimation. To estimate the cost of a software project, COCOMO II uses cost drivers, line of code and scale factors. However, the COCOMO II is still lacking in terms of accuracy. To improve the accuracy of COCOMO II, this research studies the influence of cost drivers and scale factors on improving the accuracy of effort estimation. Then, This Research using Fuzzy Logic and Local Calibration to improve the accuracy of COCOMO II. Fuzzy Logic with Gaussian Membership Functions (GMF) has been applied to redesign the effort multiplier. This research also implements Local Calibration to make a better accuracy of COCOMO II by using the value of the dataset as the input for the calculation of the calibration. This research gives a value effort multiplier that better suited for use in estimating and improving the method for search a new value of parameters to calculation COCOMO II that is more suitable for a category of a dataset used in this research. The Local Calibration result can be used for a future software project with similar category with the dataset used in this research. The result is a proposed model contributes on decreasing error significantly (about only 10% from MMRE COCOMO II or fuzzy model).

Improving the accuracy of COCOMO II using fuzzy logic and local calibration method

Business process management technology has been developing and growing. In the industry, business processes continue to change over time. Changes occur due to consumer demand, technological innovation, and policy changes. Therefore, existing business processes should be able to adapt to any changes. Currently, the design phase is always ignored as the focused point in the business process development. The development process has only focused on the implementation. Hence, in this paper, we propose a method to form a reconfigurable business process model. The method consist of a series of sequential process. The process started with calculating similarity value of business processes based on its structure. Then, we perform the clustering process to the models based on similarity values. From every cluster, we extract the common fragment using dependency graph calculation. The experimental result shows that the proposed method can produce common fragments or reconfigurable models which can be rearranged to its variations. The common fragment has high flexibility and granularity. Research on reconfigurable models of business processes are still limited, therefore the result of this research can be used to start other researches.

Workflow common fragments extraction based on WSDL similarity and graph dependency

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