Mining developer communication data streams

This paper explores the concepts of modelling a software development project as a process that results in the creation of a continuous stream of data. In terms of the Jazz repository used in this research, one aspect of that stream of data would be developer communication. Such data can be used to create an evolving social network characterized by a range of metrics. This paper presents the application of data stream mining techniques to identify the most useful metrics for predicting build outcomes. Results are presented from applying the Hoeffding Tree classification method used in conjunction with the Adaptive Sliding Window (ADWIN) method for detecting concept drift. The results indicate that only a small number of the available metrics considered have any significance for predicting the outcome of a build

Search Based Software Engineering

Abstract This paper reviews the search based software engineering research and finds the major milestones in this direction. The SBSE approach has been the topic of several surveys and reviews. Search Based Software Engineering (SBSE) consists of the application of search-based optimization to software engineering. Using SBSE, a software engineering task is formulated as a search problem by defining a suitable candidate solution representation and a fitness function to differentiate between solution candidates. This paper gives an overview of major research studies undertaken in the domain

The role of Artificial Intelligence in Software Engineering

There has been a recent surge in interest in the application of Artificial Intelligence (AI) techniques to Software Engineering (SE) problems. The work is typified by recent advances in Search Based Software Engineering, but also by long established work in Probabilistic reasoning and machine learning for Software Engineering. This paper explores some of the relationships between these strands of closely related work, arguing that they have much in common and sets out some future challenges in the area of AI for SE. © 2012 IEEE

Software Development Analytics in Practice: A Systematic Literature Review

Context:Software Development Analytics is a research area concerned with providing insights to improve product deliveries and processes. Many types of studies, data sources and mining methods have been used for that purpose. Objective:This systematic literature review aims at providing an aggregate view of the relevant studies on Software Development Analytics in the past decade (2010-2019), with an emphasis on its application in practical settings. Method:Definition and execution of a search string upon several digital libraries, followed by a quality assessment criteria to identify the most relevant papers. On those, we extracted a set of characteristics (study type, data source, study perspective, development life-cycle activities covered, stakeholders, mining methods, and analytics scope) and classified their impact against a taxonomy. Results:Source code repositories, experimental case studies, and developers are the most common data sources, study types, and stakeholders, respectively. Product and project managers are also often present, but less than expected. Mining methods are evolving rapidly and that is reflected in the long list identified. Descriptive statistics are the most usual method followed by correlation analysis. Being software development an important process in every organization, it was unexpected to find that process mining was present in only one study. Most contributions to the software development life cycle were given in the quality dimension. Time management and costs control were lightly debated. The analysis of security aspects suggests it is an increasing topic of concern for practitioners. Risk management contributions are scarce. Conclusions:There is a wide improvement margin for software development analytics in practice. For instance, mining and analyzing the activities performed by software developers in their actual workbench, the IDE

An online corpus of UML Design Models : construction and empirical studies

We address two problems in Software Engineering. The first problem is how to assess the severity of software defects? The second problem we address is that of studying software designs. Automated support for assessing the severity of software defects helps human developers to perform this task more efficiently and more accurately. We present (MAPDESO) for assessing the severity of software defects based on IEEE Standard Classification for Software Anomalies. The novelty of the approach lies in its use of uses ontologies and ontology-based reasoning which links defects to system level quality properties. One of the main reasons that makes studying of software designs challenging is the lack of their availability. We decided to collect software designs represented by UML models stored in image formats and use image processing techniques to convert them to models. We present the 'UML Repository' which contains UML diagrams (in image and XMI format) and design metrics. We conducted a series of empirical studies using the UML Repository. These empirical studies are a drop in the ocean empirical studies that can be conducted using the repository. Yet these studies show the versatility of useful studies that can be based on this novel repository of UML designs.Erasmus Mundus program (JOSYLEEN)Algorithms and the Foundations of Software technolog