Software evolution prediction using seasonal time analysis: a comparative study

Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure

The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes

Automatic testing is a widely adopted technique for improving software quality. Software developers add, remove and update test methods and test classes as part of the software development process as well as during the evolution phase, following the initial release. In this work we conduct a large scale study of 61 popular open source projects and report the relationships we have established between test maintenance, production code maintenance, and semantic changes (e.g, statement added, method removed, etc.). performed in developers' commits. We build predictive models, and show that the number of tests in a software project can be well predicted by employing code maintenance profiles (i.e., how many commits were performed in each of the maintenance activities: corrective, perfective, adaptive). Our findings also reveal that more often than not, developers perform code fixes without performing complementary test maintenance in the same commit (e.g., update an existing test or add a new one). When developers do perform test maintenance, it is likely to be affected by the semantic changes they perform as part of their commit. Our work is based on studying 61 popular open source projects, comprised of over 240,000 commits consisting of over 16,000,000 semantic change type instances, performed by over 4,000 software engineers.Comment: postprint, ICSME 201

Software defect prediction using maximal information coefficient and fast correlation-based filter feature selection

Software quality ensures that applications that are developed are failure free. Some modern systems are intricate, due to the complexity of their information processes. Software fault prediction is an important quality assurance activity, since it is a mechanism that correctly predicts the defect proneness of modules and classifies modules that saves resources, time and developers’ efforts. In this study, a model that selects relevant features that can be used in defect prediction was proposed. The literature was reviewed and it revealed that process metrics are better predictors of defects in version systems and are based on historic source code over time. These metrics are extracted from the source-code module and include, for example, the number of additions and deletions from the source code, the number of distinct committers and the number of modified lines. In this research, defect prediction was conducted using open source software (OSS) of software product line(s) (SPL), hence process metrics were chosen. Data sets that are used in defect prediction may contain non-significant and redundant attributes that may affect the accuracy of machine-learning algorithms. In order to improve the prediction accuracy of classification models, features that are significant in the defect prediction process are utilised. In machine learning, feature selection techniques are applied in the identification of the relevant data. Feature selection is a pre-processing step that helps to reduce the dimensionality of data in machine learning. Feature selection techniques include information theoretic methods that are based on the entropy concept. This study experimented the efficiency of the feature selection techniques. It was realised that software defect prediction using significant attributes improves the prediction accuracy. A novel MICFastCR model, which is based on the Maximal Information Coefficient (MIC) was developed to select significant attributes and Fast Correlation Based Filter (FCBF) to eliminate redundant attributes. Machine learning algorithms were then run to predict software defects. The MICFastCR achieved the highest prediction accuracy as reported by various performance measures.School of ComputingPh. D. (Computer Science