Graph-based analysis and prediction for software evolution

Abstract

We exploit recent advances in analysis of graph topology to better understand software evolution, and to construct predictors that facilitate software development and maintenance. Managing an evolving, collaborative software system is a complex and expensive process, which still cannot ensure software reliability. Emerging techniques in graph mining have revolutionized the modeling of many complex systems and processes. We show how we can use a graph-based characterization of a software system to capture its evolution and facilitate development, by helping us estimate bug severity, prioritize refactoring efforts, and predict defect-prone releases. Our work consists of three main thrusts. First, we construct graphs that capture software structure at two different levels: (a) the product, i.e., source code and module level, and (b) the process, i.e., developer collaboration level. We identify a set of graph metrics that capture interesting properties of these graphs. Second, we study the evolution of eleven open source programs, including Firefox, Eclipse, MySQL, over the lifespan of the programs, typically a decade or more. Third, we show how our graph metrics can be used to construct predictors for bug severity, high-maintenance software parts, and failureprone releases. Our work strongly suggests that using graph topology analysis concepts can open many actionable avenues in software engineering research and practice.

Evaluation of Software Degradation and Forecasting Future Development Needs in Software Evolution

“…In a research paper, [14] presented how graph-based characterization can be used to capture software system evolution and facilitate development that helps estimate bug severity, prioritize refactoring efforts, and predict defect-prone releases. Also, [15] presented a set of approaches to address some problems in high-confidence software evolution.…”

Towards the characterization of realistic models

“…The authors of [7] use graph metrics to capture the structure and evolution of software products and processes in order to detect significant structural changes, help estimate bug severity, prioritize debugging efforts, and predict defectprone releases in software engineering. Additionally, the principles of complex networks are used to measure the structural complexity of software systems [25,26] and to predict defects on dependency graphs [41].…”

Analyzing Trends in Software Product Lines Evolution Using aCladistics Based Approach

“…Bhattacharya [12] presented a graph based analysis for software evolution. The approach consists of building two types of graphs, the source code-based graph which captures communication between the program functions and modules, and the developer collaboration graph which shows how developers communicate as software evolves.…”