Architecture Anti-Patterns: Automatically Detectable Violations of Design Principles

Abstract

In large-scale software systems, error-prone or change-prone files rarely stand alone. They are typically architecturally connected and their connections usually exhibit architecture problems causing the propagation of error-proneness or change-proneness. In this paper, we propose and empirically validate a suite of architecture anti-patterns that occur in all large-scale software systems and are involved in high maintenance costs. We define these architecture anti-patterns based on fundamental design principles and Baldwin and Clark's design rule theory. We can automatically detect these anti-patterns by analyzing a project's structural relationships and revision history. Through our analyses of 19 large-scale software projects, we demonstrate that these architecture anti-patterns have significant impact on files' bug-proneness and change-proneness. In particular, we show that 1) files involved in these architecture anti-patterns are more error-prone and change-prone; 2) the more anti-patterns a file is involved in, the more error-prone and change-prone it is; and 3) while all of our defined architecture anti-patterns contribute to file's error-proneness and change-proneness, Unstable Interface and Crossing contribute the most by far.

Inspection guidelines to identify security design flaws

“…Architectural bad smells and anti-patterns. The literature on architectural bad smells [9,19] and anti-patterns [5,13,14] collides with our work for what concerns the ambition to find and remove architectural issues that negatively impact system life-cycle properties (extensibility, maintainability, testability, etc.). In contrast, our work investigates security design flaws, that is architectural design decisions that negatively impact the system security.…”

On the Lack of Consensus Among Technical Debt Detection Tools

“…We also collected size and complexity data from Designite. DV8: DV8 performs the calculation of decoupling level [17] and propagation cost [18], the identification of architectural roots [19], and the identification of design anti-patterns [16]. Our study only considers the six anti-patterns: 1) Clique-files forming a strongly connected component, 2) Unhealthy Inheritance-violations of the Liskov Substitution Principle [20], 3) Package Cycle-two folders mutually depend on each other, 4) Unstable Interface-a file with many dependents that changes often with all of them, 5) Crossing-a file with a high fanin and fan-out that changes often with its dependents and dependees, and 6) Modularity Violation-files that change together frequently but have no structural relationship.…”

“…For example, Designite [5] defines smells at architectural, design, and code levels, each based on different sets of rules. DV8 [10], [16] defines a suite of anti-patterns that are claimed to be design debts. Structure101 [4] considers tangles and excessive complexity as the measures of technical debt.…”

Towards Automated Security Design Flaw Detection

“…The identification of problematic areas in design-level representations of a system with architectural smells [25]- [28] or anti-patterns [29], [30] has previously been used to identify software engineering issues, such as maintainability, and to assist in refactoring. For example, Garcia et al [25] introduce a catalog of architectural bad smells specified with UML diagrams.…”

“…Similarly, Bouhours et al [27] contribute with a catalog of 23 "spoiled patterns" or, architectural design antipatterns. Yet, the existing literature about architectural design flaws [25], [27], [30]- [32] lacks a systematized knowledge base about security-relevant architectural design flaws supporting automated assessment.…”