A framework of classifying maintenance requests based on learning techniques

Classify maintenance request is one of the processes in the large software system to support maintainers in doing their daily maintenance tasks more effectively. Categorizing these maintenance requests are an essential requirement in managing the maintenance request for software maintainer and need a great effort as well as determining classification. Hence, this paper presents the framework from the use of three different classification approaches, namely Bayesian model Decision Tree and Logistic regression. We show that naïve Bayesian classifier, Decision Tree and Logistic regression can be used to accurately classify issues into maintenance type

Text-based classification incoming maintenance requests to maintenance type

Classifying maintenance request is one of the important task in the large software system, yet often in large software system are not well classified. This is due to difficult in classifying by software maintainer. The categorization of maintenance type is effect on determine the corrective, adaptive, perfective, and preventive which are important to determine various quality factors of the system. In this paper we found that the requests for maintenance support could be classified correctly into corrective and adaptive. We used two different machine learning techniques alternatively Naïve Bayesian and Decision tree to classify issues into two type. Machine learning approach used the features that could be effective in increasing the accuracy of the system. We used 10-fold cross validation to evaluate the system performance. 1700 issues from shipment monitoring system were used to asses the accuracy of the system

Sequential pattern mining on library transaction data

Application of data mining techniques in library data results interesting and useful patterns that can be used to improve services in university libraries. This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students are generated for minimum supports 0.3, 0.2, 0.15 and 0.1. These patterns can help library in providing book recommendation to students, conducting book procurement based on readers need, as well as managing books layout

Effects of extended features on text-based classifier for corrective maintenance

Software maintenance (SM) is a complex process and is composed of various tasks that are supported by software maintainer. Classification of maintenance request (MR) is one of the tasks in large software system, yet it is often not well classified. Classification of the MRs depends on their types, which are corrective, adaptive, perfective or preventive, which are also known as maintenance type (MT). The MTs are important in keeping the quality factors of the software system. Especially, corrective maintenances are the most requests which are released in bug tracking system (BTS) in comparison to other MTs. Corrective maintenance indicates the modification of software product after its delivery in order to correct the discovered faults, and non-corrective indicated other types of maintenance. However, classification of MT is difficult in nature and this affect maintainability of the system. A number of researches in this area are dedicated to automate methods and processes in SM in order to aid MT classification. Thus, there is a need for tools that support the maintainers in doing their daily maintenance activities more effectively. The tools should be able to classify MRs automatically without human intervention in performing MT classification. MR is textual information that can be categorized according to various features by using machine learning (ML) techniques. Title, description, error encountered, and source of request are four features for the datasets which are used to train the classifiers. The two recent features, error encountered and source of request are considerate as new features in this study. These new features are added to two previous features (title and description) which were used by Antoniol et al., (2008). The goal of this research is to increase the classification accuracy of MRs into MT by using these features and present the effect of each feature in determining MT and show the best feature among the two new features. Next, the textual information of the reported bugs in the BTS will be also considered to determine whether it is sufficient to classify the MRs into corrective or non-corrective MTs. This research also presents the results of applied combining new features in the MR classification, which affect the maintainability and other quality factors of the software system. Three different classification techniques, namely Decision Tree, Naïve Bayesian, and Logistic Regression are used as the classifier. The dataset used in the experiment are from three BTS, which are Mozilla, Eclipse and JBOSS. The dataset comprises of 1800 MRs with the corresponding features. The experimental results show that the proposed MRMT model is able to achieve higher classification accuracy. The MRMT model, which is employed two more features, namely source of request and error encountered, has also outperformed the previous work

A model of managing knowledge for software maintenance as a service (SMaaS) in a private cloud computing environment

Software Maintenance (SM) community of practice (CoP) is including the system maintainer as a service provider and the users as its service recipient. Based on this scenario, they are working together or work collaboratively in order to optimize the capabilities of the software, which we called it as SM as a service (SMaaS) process. In this context, The CoP can make use knowledge management system (KMS) as a tool in managing the SM knowledge as a best practice and lesson learnt. SM is the process of identifying and delivering the software as a product based on service level agreement (SLA) that has been made between service provider and the users. The paper will discuss the model on how the SM is offering its service of processes through knowledge life cycle which starting from knowledge acquisition, knowledge storing, knowledge dissemination, and knowledge application in order to avoid any shortcoming fault or failure especially during the software development (SD) in a private cloud computing environment. Therefore, by using the KMS model in managing knowledge of SM, CoP can utilize the SM knowledge in the KMS and it will reduces the mistake or errors, so that they can also maintain a good service besides in enhancing the return of investment (ROI) as well as the quality of software to the particular users