Abstract Software maintenance is a challenging and laborious software management activity, especially for open‐source software. The bugs reports of such software allow tracking maintenance activities and were used in several empirical studies to better predict the bug resolution effort. These reports are known for their large size and contain nonrelevant instances that need to be preprocessed to be suitable for use. To this end, instance selection (IS) has been proposed in the literature as a way to reduce the size of the datasets, while keeping the relevant instances. The objective of this study is to perform an empirical study that investigates the impact of data preprocessing through IS on the performance of bug resolution prediction classifiers. To deal with this, four IS algorithms, namely, edited nearest neighbor (ENN), repeated ENN, all‐k nearest neighbors, and model class selection, are applied on five large datasets, together with five machine learning techniques. Overall, 125 experiments were performed and compared. The findings of this study highlight the positive impact of IS in providing better estimates for bug resolution prediction classifiers, in particular using repeated ENN and ENN algorithms.

In software engineering, maintenance effort estimation is a challenging research topic. Several empirical studies have focused on Maintenance Effort Estimation for Open Source Software (O-MEE) using Machine Learning (ML) techniques. Although Tuning the Parameters (TP) of ML techniques has proved to improve their performance, it has not yet been investigated in the context of O-MEE for bug resolution prediction. Therefore, this empirical study investigates the impact of grid search TP method, on the performance of k-Nearest Neighbor, Support Vector Machine, and Random Tree using Eclipse Platform, Eclipse JDT, and Apache datasets. A set of 18 ML classifiers were built and the results show the positive impact of the TP on the ML techniques' performance compared to the default ones.

Studies about maintenance effort estimation of open-source software have investigated the impact of single instance selection on the performance machine learning techniques. However, Ensemble of Instance Selection (EIS) has not been investigated for this topic, especially for bug resolution prediction. This empirical study considers the impact of EIS on the performance of k-Nearest Neighbor, Support Vector Machine, and Multinomial Naïve Bayes techniques. A set of 27 classifiers are built using Bagging and Random Feature Subset Ensembles based on AllkNN single instance selection algorithm on three datasets. The results are presented, together with a comparative analysis of the built classifiers' performance. The results show that the investigated EIS algorithms improve the performance of ML classifiers and outperform the single instance selection algorithm-based classifiers.

Software maintainability has gained more attention in recent years. It can be defined as the ease with which modifications can be performed. In this study, we are interested in the changes of a class due to bug fixing. We performed an experimental study using five Machine learning techniques; K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Trees (DT), Multilayer Perceptron (MLP), and Naïve Bayes (NB). The main focus in this study is to propose the use of Grid search method for tuning hyper-parameters and to balance datasets using SMOTE technique. The results show that there is no evidence concerning the best ML technique in all datasets. However, we found that balanced data and tuning parameters are suitable in order to obtain the best performance of ML techniques.

Software Product Maintainability Prediction (SPMP) has received more attention from researchers to control the high costs of software maintenance. Many studies related to this topic have been published in the recent years. Some of them, referred to as secondary studies, focused on the interpretation and synthesis of available published research by giving an up-to-date state of the art about SPMP. This state of the art is provided in a form of literature survey or in a rigorous systematic literature review. The objective of this paper is to investigate these secondary studies to discuss methods that they use and contents that they cover. A set of survey research questions have been proposed and discussed through the investigation of nine selected secondary studies collected from different digital libraries. Based on the results, the analysis shows that maintainability prediction models/techniques as well as maintainability key predictors measures/factors are the most studied aspects in SPMP. Moreover, there is a need to address in depth the performance of SPMP models as well as their validation. We believe that this study will be a reliable basis for further research in software maintainability studies.

Software Process Improvement (SPI) has become one of the main strategic objectives in software industry. Companies make more investments in implementing software quality standards and models that focus on process assessment to improve their performance and productivity. To achieve these goals, companies focus on improving their process by means of improvement initiatives which may be implemented. To help practitioners find more innovative ways to manage and implement software process improvement initiatives efficiently, an important number of studies related to this topic have been emerged in recent years. Some of them, referred to as secondary studies, focused on the interpretation and synthesis of available published research works by giving an up to date state of art about SPI. This state of the art is provided in a form of literature surveys or in a methodological form using well established approaches such as systematic reviews or systematic mappings or tertiary studies. The objective of this paper is to identify and present the current secondary studies on SPI. The purpose is to discuss methods that these literature reviews of SPI use, their quality, and specific subjects that they cover. A set of survey research questions have been proposed and discussed through the investigation of 70 selected secondary studies collected from different digital libraries. The results show that success factors and issues related to implementation of SPI initiatives are the most studied, and there is a need to address in depth the measurement aspects in SPI.

An e-government portal's maturity model is a set of stages (from basic to advanced ones) that determines the maturity of e-government portals. In fact, these models can be used to provide directions and recommendations for agencies to improve their portals' maturity. However, before choosing a maturity model by any agency, it is important to know to which extent the e-government community agree or disagree with the model. In previous research studies, we have built an e-government portals' maturity model that is based on a best practice model. The aim of this paper is to validate this new model by e-government experts using a survey to prove that the model is valid and reliable. For this purpose, we have described the components of this model, and the previous work that has been done to build it. Based on the results of the survey, our findings show that the new model has proven its validity.

The two ongoing repositories of software projects in the software engineering community are the ISBSG (International Software Benchmarking Standards Group) Repository and PROMISE (Predictor Models In Software Engineering). These repositories lack structured documentation and a researcher interested in using the datasets has to conduct his own investigation to identify the datasets that are suitable for his purposes. This paper provides additional information on these datasets by identifying the topics addressed, highlighting the availability of the data file and of the description of attributes related to the datasets, and indicating their usefulness for benchmarking studies.

The data repository of the International Software Benchmarking Standards Group (ISBSG) can be used by researchers to investigate cause and effect relationships by enabling them to study which variables contribute to achieving certain objectives, such as increasing productivity and improving quality. The ISO 9126 series proposes a number of software quality models, and a large inventory of candidate derived measures for those models. However, even if in the 25000 series (an upcoming version of ISO 9126) there is a plan to better define the base and derived measures, neither the ISO 9126 nor ISO 25000 series include a data repository or intend to create one, and without data for comparison purposes or for analyzing actual relationships across quality attributes and models, the series is challenging to use in practice. This paper concurrently analyzes ISO 9126 and the ISBSG data repository on software projects in order to identify the subset of ISO 9126 quality characteristics that is referenced in the ISBSG repository. It also identifies a number of quality-related data fields from the ISBSG which can be useful in empirical and benchmarking studies.