Anuradha Chug

Application of Group Method of Data Handling model for software maintainability prediction using object oriented systems

Object-oriented methodology has emerged as most prominent in software industry for application development. Maintenance phase begins once the product is delivered and by software maintainability we mean the ease with which existing software could be modified during maintenance phase. We can improve and control software maintainability if we can predict it in the early phases of software life cycle using design metrics. Predicting the maintainability of any software has become critical with the increasing importance of software maintenance. Many authors have practiced and proved theoretical validation followed by empirical evaluation using statistical and experimental techniques for evaluating the relevance of any given metrics suite using many models. In this paper, we have presented an empirical study to evaluate the effectiveness of novel technique called Group Method of Data Handling (GMDH) for the prediction of maintainability over other models. Although many metrics have been proposed in the literature, software design metrics suite proposed by Chidamber et al. and revised by Li et al. have been selected for this study. Two web-based customized softwares developed using C# Language have been used for empirical study. Source code of old and new versions for both applications were collected and analysed against modifications made in every class. The changes were counted in terms of number of lines added, deleted or modified in the classes belonging to new version with respect to the classes of old version. Finally values of metrics were combined with “change” in order to generate data points. Hence, in this study an attempt has been made to evaluate and examine the effectiveness of prediction models for the purpose of software maintainability using real life web based projects. Three models using Feed Forward 3-Layer Back Propagation Network (FF3LBPN), General Regression Neural Network (GRNN) and GMDH are developed and performance of GMDH is compared against two others i.e. FF3LBPN and GRNN. With the aid of this empirical analysis, we can safely suggest that software professionals can use OO metric suite to predict the maintainability of software using GMDH technique with least error and best precision in an object oriented paradigm.

Prioritization of Classes for Refactoring: A Step towards Improvement in Software Quality

Bad Smells are certain structures in the software which violates the design principles and ruin the software quality. In order to deals with the bad smells, often refactoring treatment is provided in the code which further improves the software quality. However, its not possible to refactor each and every class of the software in maintenance phase due to certain deadlines. Prioritization of classes helps the developer to identify the software portions requiring urgent refactoring. In the current study, we propose a framework to identify the potential classes which immediately require refactoring based on the bad smells as well as design characteristics. We evaluate our approach on medium sized open-source systems ORDrumbox. Four types of code-smells Feature Envy, Long Method, God Class and Type Checking were identified and well known Chidamber and Kemerer metric suite is used to evaluate the object oriented characteristics. Both are combined in certain ratio to calculate new proposed metric Quality Depreciation Index Rule (QDIR) for each class. Classes are further arranged as per their QDIR values to identify the severely affected classes requiring immediate refactoring treatment. This study works on 80:20 principles conveying 80% of the code quality can be improved by just providing refactoring treatment to 20% of the severely affected classes. Results reflects that the bad smells and design metrics can be used as an important source of information to quantify the flaws in the classes, thus helpful to maintainers in performing their task under strict time constraints while maintaining the overall software quality.

An empirical investigation of evolutionary algorithm for software maintainability prediction

Software maintenance is one of the tedious as well as costly phases in the software development life cycle. It starts immediately after the software product is delivered to the customer and ends when the product is no longer in use. There are various activities carried out during software maintenance phase such as the addition of new features, deletion of obsolete features, correction of errors, adaption to new environment etc. Software maintainability is the quality attribute of the software product which determines the ease with which these modifications can be performed. If we can predict the maintainability accurately, cost and time associated with the maintenance activity can be highly reduced. The main aim of this study is to propose the use of evolutionary technique particularly genetic algorithm for the software maintainability prediction and compare its performance with various machine leaning techniques such as Decision Table, Radial Basis Function Neural Network, Bayes Net and Sequential Minimal Optimization (SMO). In order to carry out this empirical investigation, datasets from four open source software systems are collected. The maintenance effort is calculated by counting the number of changes in terms of line of code from one version of the software to another. Based on the experiments conducted, we conclude that the evolutionary algorithm outperformed all the other classifiers, thus, very useful for the concise prediction of software maintainability. Results of this would be helpful to practitioners as they can use the maintainability prediction in order to achieve precise planning of resource allocation.

Sequencing of refactoring techniques by Greedy algorithm for maximizing maintainability

Software maintainability is the ease with which a software system can be modified to correct faults, improve performance or other attributes of the source code. Bad smells are symptoms of deeper problem that indicates the need for refactoring which is the process of changing internal structure of the software without affecting its external attributes. Applying different refactoring techniques in different parts of a code results in changed maintainability value every time. Therefore, sequence in which refactoring should be applied is important so that optimal results can be obtained. In this study, we have proposed an approach for evaluating sequence of refactoring by with the help of greedy algorithm. The algorithm selects locally optimal solution at each stage with the hope of finding global optimal solution. Different sequences are generated and applied to the source code to calculate sum of software maintainability values. Greedy algorithm helps in finding the optimal sequence out of all the search space. We have evaluated the approach with source code having god class, long method, feature envy, long parameter list, data clumps, data class, class hierarchy problem, empty catch block, exception thrown in finally block and nested try statement bad smells which are detected manually. Hence our approach is able to identify sequence for refactoring as well as best refactoring which will increase maintainability and enhance software quality.

Analyzing and evaluating security features in software requirements

Software requirements, for complex projects, often contain specifications of non-functional attributes (e.g., security-related features). The process of analyzing such requirements is laborious and error prone. Due to the inherent free-flowing nature of software requirements, it is tempting to apply Natural Language Processing (NLP) based Machine Learning (ML) techniques for analyzing these documents from the point of view of comprehensiveness and consistency. In this paper, we propose novel semi-automatic methodology that can assess the security requirements of the software system from the perspective of completeness, contradiction, and inconsistency. Security standards introduced by the ISO are used to construct a model for classifying security-based requirements using NLP-based ML techniques. Hence, this approach aims to identify the appropriate structures that underlie software requirement documents. Once such structures are formalized and empirically validated, they will provide guidelines to software organizations for generating comprehensive and unambiguous requirement specification documents as related to security-oriented features. The proposed solution will assist organizations during the early phases of developing secure software and reduce overall development effort and costs.

A Metric Suite for Predicting Software Maintainability in Data Intensive Applications

Software maintainability is the vital aspect of software quality and defined as the ease with which modifications can be made once the software is delivered. Tracking the maintenance behaviour of a software product is very complex that is widely acknowledged by the researchers. Many research studies have empirically validated that the prediction of object oriented software maintainability can be achieved before actual operation of the software using design metrics proposed by Chidamber and Kemerer (C&K). However, the framework and reference architecture in which the software systems are being currently developed have changed dramatically in recent times due to the emergence of data warehouse and data mining field. In the prevailing scenario, certain deficiencies were discovered when C&K metric suite was evaluated for data intensive applications. In this study, we propose a new metric suite to overcome these deficiencies and redefine the relationship between design metrics with maintainability. The proposed metric suite is evaluated, analyzed and empirically validated using five proprietary software systems. The results show that the proposed metric suite is very effective for maintainability prediction of all software systems in general and for data intensive software systems in particular. The proposed metric suite may be significantly helpful to the developers in analyzing the maintainability of data intensive software systems before deploying them.

Prioritization of code restructuring for severely affected classes under release time constraints

Bad smells help us to look deeper into the problem as they are threat to design principles and quality of software. Redesigning helps us in restructuring code without any change in its external behavior. It is not feasible to restructure each class of the software due release time constraints. Therefore, there is a need to prioritize classes so that effort and quality can be improved. In this study, we have proposed a framework based on the combination of C & K metric suite and eleven types of bad smells. A new metric, Quality Decline Factor (QDF), has been proposed which identify the severity of the classes and helps the software maintenance team to focus only on severely affected code. Pareto analysis is also valid in this study which states that 80% of the code quality can be improved by providing redesigning treatments to 20% of the severely affected code. This study will reduce the work of software maintenance practitioners and will help them to complete their work on time.

An empirical study to assess the effects of refactoring on software maintainability

Maintenance is the most expensive phase of software and during this process refactoring is performed to improve the code without affecting its external behaviour. This study examines the effects of refactoring on maintainability using five proprietary software systems. Internal quality attributes were measured using design metrics suite whereas external quality attributes such as the level of abstraction, understandability, modifiability, extensibility and reusability were measured through expert opinion. The original versions of software are compared with refactored versions and the changes in quality attributes were mapped to maintainability. The results reveal that refactoring significantly improves the software quality and enhances software life. It was also found that even though refactoring is very tedious and might introduce errors if not implemented with utmost care, it is still advisable to frequently refactor the code to increase maintainability. Results of this study are useful to project managers in identifying the opportunities of refactoring while maintaining a perfect balance between reengineering and over-engineering.

Dynamic metrics are superior than static metrics in maintainability prediction: An empirical case study

Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions like budget planning, cost estimation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Many design metrics have proposed in literature to measure various constructs of Object Oriented (OO) paradigm such as class, coupling, cohesion, inheritance, information hiding and polymorphism and use them further in determining the various aspects of software quality. However, the use of conventional static metrics have found to be inadequate for modern OO software due to the presence of run time polymorphism, templates class, template methods, dynamic binding and some code left unexecuted due to specific input conditions. This gap gave a cue to focus on the use of dynamic metrics instead of traditional static metrics to capture the software characteristics and further deploy them for maintainability predictions. As the dynamic metrics are more precise in capturing the execution behavior of the software system, in the current empirical investigation with the use of open source code, we validate and verify the superiority of dynamic metrics over static metrics. Four machine learning models are used for making the prediction model while training is performed simultaneously using static as well as dynamic metric suite. The results are analyzed using prevalent prediction accuracy measures which indicate that predictive capability of dynamic metrics is more concise than static metrics irrespective of any machine learning prediction model. Results of this would be helpful to practitioners as they can use the dynamic metrics in maintainability prediction in order to achieve precise planning of resource allocation.

Software defect prediction analysis using machine learning algorithms

Software Quality is the most important aspect of a software. Software Defect Prediction can directly affect quality and has achieved significant popularity in last few years. Defective software modules have a massive impact over softwares quality leading to cost overruns, delayed timelines and much higher maintenance costs. In this paper we have analyzed the most popular and widely used Machine Learning algorithms — ANN (Artificial Neural Network), PSO(P article Swarm Optimization), DT (Decision Trees), NB(Naive Bayes) and LC (Linear classifier). The five algorithms were analyzed using KEEL tool and validated using k-fold cross validation technique. Datasets used in this research were obtained from open source NASA Promise dataset repository. Seven datasets were selected for defect prediction analysis. Classification was performed on these 7 datasets and validated using 10 fold cross validation. The results demonstrated the dominance of Linear Classifier over other algorithms in terms of defect prediction accuracy.