An approach to predict software maintenance cost based on ripple complexity

2002 ◽  
Author(s):  
T. Hirota ◽  
M. Tohki ◽  
C.M. Overstreet ◽  
M. Hashimoto ◽  
R. Cherinka
Keyword(s):  
Software Maintenance ◽  
Maintenance Cost

Using Ensembles for Class-Imbalance Problem to Predict Maintainability of Open Source Software

2020 ◽  
Vol 27 (05) ◽  
pp. 2040011
Author(s):  
Ruchika Malhotra ◽  
Kusum Lata
Keyword(s):  
Software Maintenance ◽  
Software Metrics ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Maintenance Cost ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Minority Class ◽  
Imbalance Problem ◽  
Ensemble Techniques

To facilitate software maintenance and save the maintenance cost, numerous machine learning (ML) techniques have been studied to predict the maintainability of software modules or classes. An abundant amount of effort has been put by the research community to develop software maintainability prediction (SMP) models by relating software metrics to the maintainability of modules or classes. When software classes demanding the high maintainability effort (HME) are less as compared to the low maintainability effort (LME) classes, the situation leads to imbalanced datasets for training the SMP models. The imbalanced class distribution in SMP datasets could be a dilemma for various ML techniques because, in the case of an imbalanced dataset, minority class instances are either misclassified by the ML techniques or get discarded as noise. The recent development in predictive modeling has ascertained that ensemble techniques can boost the performance of ML techniques by collating their predictions. Ensembles themselves do not solve the class-imbalance problem much. However, aggregation of ensemble techniques with the certain techniques to handle class-imbalance problem (e.g., data resampling) has led to several proposals in research. This paper evaluates the performance of ensembles for the class-imbalance in the domain of SMP. The ensembles for class-imbalance problem (ECIP) are the modification of ensembles which pre-process the imbalanced data using data resampling before the learning process. This study experimentally compares the performance of several ECIP using performance metrics Balance and g-Mean over eight Apache software datasets. The results of the study advocate that for imbalanced datasets, ECIP improves the performance of SMP models as compared to classic ensembles.

Web Based Application Maintenance Cost Estimation Modeling Using Bayesian SEM

2011 ◽  
Vol 403-408 ◽  
pp. 3704-3708
Author(s):  
Somchai Prakancharoen
Keyword(s):  
Bayesian Analysis ◽  
Cost Estimation ◽  
Software Maintenance ◽  
Structural Equation ◽  
Likelihood Estimation ◽  
Equation Modeling ◽  
Maintenance Cost ◽  
Web Based ◽  
Group Modeling ◽  
Model Training

The objectives of this research were to find out the Structural equation modeling coefficient and other parameter estimation under unknown prior distribution and compare this new model’s coefficient accuracy with the former model on “Web based application maintenance cost estimation multi group modeling” [16]. This new coefficient and parameter were estimated with Bayesian analysis instead of Maximum likelihood estimation (ML). The input model used in Bayesian analysis was started from the ML-model result [16]. The new values were replaced into the former model then MMRE was detected from 30 (testing) completed software maintenance projects while 192 projects were used for SEM model training. The result of cross validation was about 44.08% for Bayesian analysis refined SEM model while the ML-SEM model was 47.58%.

scholarly journals Rule Based Event Log Generation Technique using Reverse Engineering

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1176-1179
Keyword(s):  
Software Maintenance ◽  
Fault Injection ◽  
Source Code ◽  
Maintenance Cost ◽  
Software Systems ◽  
Primary Input ◽  
Rule Based ◽  
Generation Technique ◽  
Rule Based Approach ◽  
Injection Experiment

In many software systems logging has been implemented inaccurately, their effectiveness during the maintenance period to identify the failures and address them quickly is very less. This in turn increases the software maintenance cost and reduces reliability of the system as many errors are unreported. This paper aims at proposing and studying a rule based approach to make the logs more effective. The source code of the target systems gets reverse engineered and acts as the primary input for this approach to introduce the automated logs into the source code. This is instrumented by a logger code driven by a set of predefined rules which are woven around the life cycle of the system entities. The validity of the approach is verified by means of a preliminary fault injection experiment into a real world system.

scholarly journals An Imperfect Debugging Software Reliability Growth Model: Optimal Release Problems through Warranty Period based on Software Maintenance Cost Model

2021 ◽  
Vol 11 (4) ◽  
pp. 4623-4631
Author(s):  
Ahmad Raad Raheem ◽  
Dr. Shaheda Akthar ◽  
Dr. Shaik Mohammad Rafi
Keyword(s):  
Software Development ◽  
Growth Model ◽  
Software Maintenance ◽  
Cost Model ◽  
Maintenance Cost ◽  
Release Time ◽  
Reliability Growth ◽  
Imperfect Debugging ◽  
Program Cost ◽  
Software Program

Software come to be an important element in recent times, from small residence hold gadgets to large machinery wishes fine software. software development is a technical oriented system where range of quantitative and qualitative duties have been completed parallel a good way to meets the needs of the consumer. Many people play a vital role within the improvement of software program product, consequently there is chance of committing errors by way of these humans and these errors becomes faults in later stages. Computing software program cost for the duration of software development can facilitate us predicting the time of release of the software. In this paper we have investigated release time of software program by way of considering the imperfect debugging software program reliability growth model and cost model.

scholarly journals Automating the Refactoring Process

2017 ◽  
Vol 23 (2) ◽  
pp. 715-735
Author(s):  
Gábor Szőke
Keyword(s):  
Software Development ◽  
Software Maintenance ◽  
Source Code ◽  
Third Party ◽  
Maintenance Cost ◽  
Special Technique ◽  
Code Smells ◽  
Analysis Techniques ◽  
Support Issue ◽  
Automatic Elimination

To decrease software maintenance cost, software development companies use static source code analysis techniques. Static analysis tools are capable of finding potential bugs, anti-patterns, coding rule violations, and they can also enforce coding style standards. Although there are several available static analyzers to choose from, they only support issue detection. The elimination of the issues is still performed manually by developers. Here, we propose a process that supports the automatic elimination of coding issues in Java. We introduce a tool that uses a third-party static analyzer as input and enables developers to automatically fix the detected issues for them. Our tool uses a special technique, called reverse AST-search, to locate source code elements in a syntax tree, just based on location information. Our tool was evaluated and tested in a two-year project with six software development companies where thousands of code smells were identified and fixed in five systems that have altogether over five million lines of code.

Software Maintenance Cost Estimation

2011 ◽  
pp. 201-227 ◽  
Author(s):  
Harry M. Sneed
Keyword(s):  
Cost Estimation ◽  
Software Maintenance ◽  
Impact Analysis ◽  
Empirical Studies ◽  
Maintenance Cost ◽  
Maintenance Costs ◽  
Estimation Process ◽  
Industrial Experiments ◽  
The Subject ◽  
Project Estimation

This chapter deals with the subject of estimating the costs of software maintenance. It reviews the existing literature on the subject and summarises the various approaches taken to estimate maintenance costs starting with the original COCOMO approach in 1981. It then deals with the subject of impact analysis and why it is essential to estimate the scope of maintenance projects. Examples are given to illustrate this. It then goes on to describe some of the tools the author has developed in the past ten years to support his practice of maintenance project estimation including the tools SoftCalc and MainCost. For both of these tools empirical studies of industrial experiments are presented as proof of the need to automate the estimation process.

SOFTWARE RELIABILITY ISSUES UNDER OPERATIONAL AND TESTING CONSTRAINTS

2005 ◽  
Vol 22 (01) ◽  
pp. 33-49 ◽  
Author(s):  
MADHU JAIN ◽  
KRITI PRIYA
Keyword(s):  
Software Reliability ◽  
Software Maintenance ◽  
Growth Parameter ◽  
Likelihood Estimation ◽  
Operational Reliability ◽  
Maintenance Cost ◽  
Release Time ◽  
Testing Time ◽  
Testing Phase ◽  
Time Problem

Software reliability plays an important role in assuring the quality of a software. To ensure software reliability, the software is tested thoroughly during the testing phase. The time invested in the testing phase or the optimal software release time depends on the level of reliability to be achieved. There are two different concepts related to software reliability, viz., testing reliability and operational reliability. In this paper, we compare both types of software reliabilities to determine the optimal testing time of the software so as to minimize the total expected software maintenance cost. We consider a software has a number of clusters of modules, each having a different number of errors and a different failure rate. A hyperexponential model is employed for analyzing software reliability growth. Parameter estimation using the maximum likelihood estimation technique is also discussed. Numerical illustrations are taken to explore the effect of various parameters on reliability and maintenance cost. It is noticed that the operational reliability concept should be adopted for the software testing time problem.

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