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

Laval, Jannik; Falleri, Jean‐Rémy; Vismara, Philippe; Ducasse, Sté́phane

doi:10.5381/jot.2012.11.1.a4

Cited by 10 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: * Affected Classes Ratio †mentioning

confidence: 99%

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study

Sas

Avgeriou

Fontana

2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

Architectural smells may substantially increase maintenance effort and thus require extra attention for potential refactoring. While we currently understand this concept and have identified different types of such smells, we have not yet studied their evolution in depth. This is necessary to inform their prioritisation and refactoring. This study analyses the evolution of individual architectural smell instances over time, and the characteristics that define these instances. Three different types of architectural smells are taken into consideration and mined from a total of 524 versions across 14 different projects. The results show how different smell types differ in multiple aspects, such as their growth rate, the importance of the affected elements over time in the dependency network of the system, and the time each instance affects the system. They also cast valuable insights on what aspects are the most important to consider during prioritisation and refactoring activities.

show abstract

Section: * Affected Classes Ratio †mentioning

confidence: 99%

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study

Sas

Avgeriou

Fontana

2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Related Workmentioning

confidence: 99%

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

Arcaini

Gargantini

Riccobene

et al. 2017

Information and Software Technology

View full text Add to dashboard Cite

“…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*…”

Section: Affected Typementioning

confidence: 99%

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

Sas¹,

Avgeriou²

2023

Preprint

View full text Add to dashboard Cite

A key aspect of technical debt (TD) management is the ability to measure the amount of principal accumulated in a system. The current literature contains an array of approaches to estimate TD principal, however, only a few of them focus specifically on architectural TD, and none of these are fully automated, freely available, and thoroughly validated. Moreover, a recent study has shown that many of the current approaches suffer from certain shortcomings, such as relying on hand-picked thresholds. In this paper, we propose a novel approach to estimate architectural technical debt principal based on machine learning and architectural smells to address such shortcomings. Our approach can estimate the amount of technical debt principal generated by a single architectural smell instance. To do so, we adopt novel techniques from Information Retrieval to train a learning-to-rank machine learning model that estimates the severity of an architectural smell and ensure the transparency of the predictions. Then, for each instance, we statically analyse the source code to calculate the exact number of lines of code creating the smell. Finally, we combine these two values to calculate the technical debt principal. To validate the approach, we conducted a case study and interviewed 16 practitioners, from both open source and industry, and asked them about their opinions on the TD principal estimations for several smells detected in their projects. The results show that for 71% of instances, practitioners agreed that the estimations provided were representative of the effort necessary to refactor the smell.

show abstract

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

Cited by 10 publications

References 14 publications

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study

Investigating Instability Architectural Smells Evolution: An Exploratory Case Study

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

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

Contact Info

Product

Resources

About