Efficient Retrieval and Ranking of Undesired Package Cycles in Large Software Systems.

Abstract

Many design guidelines state that a software system architecture should avoid cycles between its packages. Yet such cycles appear again and again in many programs. We believe that the existing approaches for cycle detection are too coarse to assist developers to remove cycles from their programs. In this paper, we describe an efficient algorithm that performs a fine-grained analysis of cycles among application packages. In addition, we define multiple metrics to rank cycles by their level of undesirability, prioritizing cycles that are the more undesired by developers. We compare these multiple ranking metrics on four large and mature software systems in Java and Smalltalk.

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study

“…The Affected design level, similarly, is important because the cycles present at both package and class level have an impact on two different levels at once. Finally, the Number of inheritance edges characteristic is considered because inheritance edges are considered an indicator of an intentional design choice [17], thus intentional cycles that contain a high number of inheritance edges between the components may be more interesting for a developer to inspect.…”

A novel use of equivalent mutants for static anomaly detection in software artifacts

“…For experimentation, we took the four projects considered in [46] (namely, ANTLR, ArgoUML, Chocho solver, and jEdit) and we analyzed their package dependency graphs in order to detect the two considered anomalies. Since all our mutation operators are refactorings, we only had to measure the quality.…”

“…Regarding software architectures and program refactoring, the classical technique for detecting anomalies is to use static analysis and some heuristics (like estimation of cycle undesirability in [46]). There exist several attempts to automatically improve the source code structure by using mutation.…”

An architectural technical debt index based on machine learning and architectural smells

“…either class or package) PCT Depth* Depth refers to the number of packages that are an ancestor of the affected element in the system's package hierarchy (i.e. the PCT) [25]. PCT Distance*…”