An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction

Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs

Evaluation of Software Understandability Using Software Metrics

Understandability is one of the important characteristics of software quality, because it may influence the maintainability of the software. Cost and reuse of the software is also affected by understandability. In order to maintain the software, the programmers need to understand the source code. The understandability of the source code depends upon the psychological complexity of the software, and it requires cognitive abilities to understand the source code. The understandability of source code is get effected by so many factors, here we have taken different factors in an integrated view. In this we have chosen roughset approach to calculate the understandability based on outlier detection. Generally the outlier is having an abnormal behavior, here we have taken that project has may be easily understandable or difficult to understand. Here we have taken few factors, which affect understandability, an brings forward an integrated view to determine understandability

Object reational data base management systems and applications in document retrieval

http://deepblue.lib.umich.edu/bitstream/2027.42/96902/1/MBA_JayaramanaF_1996Final.pd

Customization design method for complex product systems based on a meta-model

In order to effectively reuse the design knowledge of product family life cycle development and support holistic and rapid individual product design, this article presents a new meta-model-based systemic customization design method for complex product systems within a product-pedigree. The proposed method aims to synthetically analyze the common and adaptive customer demands and product features of a product-pedigree of complex product systems and to quickly respond to the changing demands based on knowledge accumulation in the field of customization design. The key to implement such a method is (1) to construct a product-pedigree-oriented product meta-model with a four-layered architecture where it is possible to achieve a high degree of abstraction of product and (2) to develop a special technique for configuring the meta-model of the complex product systems. We have tested the proposed method with the rapid design of product-pedigree of a high-speed train’s bogies as an illustrative example. In this work, a rapid customization design prototype system has been developed and applied to the design of a high-speed train’s bogie to illustrate how to construct a product meta-model and how to conduct configuration design on different layers and variant design for generating new products

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications

Simulation of Road Traffic Applying Model-Driven Engineering

Road traffic is an important phenomenon in modern societies. The study of its different aspects in the multiple scenarios where it happens is relevant for a huge number of problems. At the same time, its scale and complexity make it hard to study. Traffic simulations can alleviate these difficulties, simplifying the scenarios to consider and controlling their variables. However, their development also presents difficulties. The main ones come from the need to integrate the way of working of researchers and developers from multiple fields. Model-Driven Engineering (MDE) addresses these problems using Modelling Languages (MLs) and semi-automatic transformations to organise and describe the development, from requirements to code. This paper presents a domain-specific MDE framework for simulations of road traffic. It comprises an extensible ML, support tools, and development guidelines. The ML adopts an agent-based approach, which is focused on the roles of individuals in road traffic and their decision-making. A case study shows the process to model a traffic theory with the ML, and how to specialise that specification for an existing target platform and its simulations. The results are the basis for comparison with related work