Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

Towards ubiquitous requirements engineering through recommendations based on context histories

The growing technological advance is causing constant business changes. The continual uncertainties in project management make requirements engineering essential to ensure the success of projects. The usual exponential increase of stakeholders throughout the project suggests the application of intelligent tools to assist requirements engineers. Therefore, this article proposes Nhatos, a computational model for ubiquitous requirements management that analyses context histories of projects to recommend reusable requirements. The scientific contribution of this study is the use of the similarity analysis of projects through their context histories to generate the requirement recommendations. The implementation of a prototype allowed to evaluate the proposal through a case study based on real scenarios from the industry. One hundred fifty-three software projects from a large bank institution generated context histories used in the recommendations. The experiment demonstrated that the model achieved more than 70% stakeholder acceptance of the recommendations.

LEMABE: a novel framework to improve analogy-based software cost estimation using learnable evolution model

One of the most important and critical factors in software projects is the proper cost estimation. This activity, which has to be done prior to the beginning of a project in the initial stage, always encounters several challenges and problems. However, due to the high significance and impact of the proper cost estimation, several approaches and methods have been proposed regarding how to perform cost estimation, in which the analogy-based approach is one of the most popular ones. In recent years, many attempts have been made to employ suitable techniques and methods in this approach in order to improve estimation accuracy. However, achieving improved estimation accuracy in these techniques is still an appropriate research topic. To improve software development cost estimation, the current study has investigated the effect of the LEM algorithm on optimization of features weighting and proposed a new method as well. In this research, the effectiveness of this algorithm has been examined on two datasets, Desharnais and Maxwell. Then, MMRE, PRED (0.25), and MdMRE criteria have been used to evaluate and compare the proposed method against other evolutionary algorithms. Employing the proposed method showed considerable improvement in estimating software cost estimation.

Adapting a Requirements Engineering Process by Key Factors Estimation

Literature mainly focuses the adaptation of any requirements engineering process on the possible variations of elicitation techniques, mainly due to information sources characteristics. However, these particularities, usually called situational factors, are seldom considered in other activities of the requirements process. Most situational factors, when considered in software projects, have a high influence on the requirements process. Therefore, the different situations that may attempt against or may favor a successful requirements process should be identified at the beginning of the project. Additionally, some of such factors may evolve along software development life cycle; this should motivate a reengineering of the requirements process at some strategic milestones. In this chapter, a process for constructing and dynamically adapting a requirements process is proposed, focusing on the evolving factors. The process follows rules based on different combinations of situational factors at specific control points and manages a repository of process blocks to perform the tailoring.

Evaluation of Determinants of Software Quality in Offshored Software Projects

Successful offshoring engagements of Indian software vendors is increasingly dependent upon the quality of the projects delivered rather than cost considerations. However, delivering quality software is reliant on effective management of various organizational, technological and people aspects. This research is to identify and evaluate the determinants of quality on software projects delivered by vendors through offshoring. Data related to recently completed projects were collected through a survey of 440 project managers from Indian vendors. Based on structural equation modeling, the authors analyze the influence determinants on specific product quality attributes. It is found that, out of six determinants, technical infrastructure and process maturity have significant influence on most of the attributes of quality in offshored IS projects from India. The authors provide a set of implications for practice and directions for further research.

COMPETENCE ASSESSMENT AND DEVELOPMENT FOR MANAGING HUMAN CAPITAL IN IT SECTOR

With ever-increasing demand for software professionals, time has come for the information technology (IT) industry to unearth competencies required for software professionals. The majority of IT companies have been employing outsourcing software projects to inexpensive hubs in developing nations. Therefore expectations from highly skilled software professionals are rising. The purpose of the study is to recognize the technical competence levels associated with software professionals in a range of profiles which are found in India's major IT organizations. There are three basic objectives of the present study. The first objective is to conduct a literature survey of description of the software engineers in IT domain. The second objective is to quantify the competency levels of the software engineers in IT sector. Finally, the third objective is to conduct a survey-based empirical study on software engineers. The professional occupation is set up through seven successive profiles.

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

eXtreme Programming, Agile Methodologies, Software Project Management, Customer Role, Rigorous Testing

Agile software development methodologies are attracting attention from academics and practitioners for planning and managing software projects. The eXtreme Programming (XP) challenges conformist wisdom regarding software system development processes and practices as agile methodologies. To work efficiently in the current software development practice, characterized by requirements fuzziness, XP moves away from document-centric operations into people-centric management. In the XP-based software project, the customers play an essential role, having multiple responsibilities such as driving the project, gathering requirements (‘user stories'), and exercising quality control (or acceptance testing). Besides, the customers must liaise with external project stakeholders (e.g., funding authorities, end-users) while maintaining the development team's trust and the wider business. The success of such software project management practices relies on the quality result of each stage of development obtained through rigorous testing. This chapter describes three characteristics of XP project management: customer role, software testing feedback, and learning.

Predicting Security Vulnerable Developers Based On Their Techno-Behavioral Characteristics

Assigning developers for highly secured software projects requires identifying developers’ tendency to contribute towards vulnerable software codes called developer-centric security vulnerability to mitigate issues on human resource management, financial and project timelines. There are problems in assessing the previous codebases in evaluating the developer-centric security vulnerability level of each developer. Thus, this paper suggests a method to evaluate this through the techno-behavioral features of their previous projects. Consequently, we present results of an exploratory study of the developer-centric security vulnerability level prediction using a dataset of 1827 developers by logically selecting 13 techno-behavioral features. Our results depict that there is a correlation between techno-behavioral features and developer-centric security vulnerability with 89.46% accuracy. This model enables to predict developer-centric security vulnerability level of any developer if the required techno-behavioral features are available avoiding the analysis of his/her previous codebases.