Publications



2018

Inbooks

Valasia Dimaridou, Alexandros-Charalampos Kyprianidis, Michail Papamichail, Themistoklis Diamantopoulos and Andreas Symeonidis
Charpter:1, pp. 25, Springer, 2018 Jan

Nowadays, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. This practice is in principle cost-effective, however it may also lead to low quality software products, if the components to be reused exhibit low quality. Thus, several approaches have been developed to measure the quality of software components. Most of them, however, rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are context-dependent and subjective. In this work, we build a mechanism that employs static analysis metrics extracted from GitHub projects and defines a target quality score based on repositories’ stars and forks, which indicate their adoption/acceptance by developers. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and examine the semantics among metrics to provide an analysis on five axes for source code components (classes or packages): complexity, coupling, size, degree of inheritance, and quality of documentation. Neural networks are thus applied to estimate the final quality score given metrics from these axes. Preliminary evaluation indicates that our approach effectively estimates software quality at both class and package levels.

@inbook{Dimaridou2018,
author={Valasia Dimaridou and Alexandros-Charalampos Kyprianidis and Michail Papamichail and Themistoklis Diamantopoulos and Andreas Symeonidis},
title={Assessing the User-Perceived Quality of Source Code Components using Static Analysis Metrics},
chapter={1},
pages={25},
publisher={Springer},
year={2018},
month={01},
date={2018-01-01},
url={https://issel.ee.auth.gr/wp-content/uploads/2019/08/ccis_book_chapter.pdf},
publisher's url={https://www.researchgate.net/publication/325627162_Assessing_the_User-Perceived_Quality_of_Source_Code_Components_Using_Static_Analysis_Metrics},
abstract={Nowadays, developers tend to adopt a component-based software engineering approach, reusing own implementations and/or resorting to third-party source code. This practice is in principle cost-effective, however it may also lead to low quality software products, if the components to be reused exhibit low quality. Thus, several approaches have been developed to measure the quality of software components. Most of them, however, rely on the aid of experts for defining target quality scores and deriving metric thresholds, leading to results that are context-dependent and subjective. In this work, we build a mechanism that employs static analysis metrics extracted from GitHub projects and defines a target quality score based on repositories’ stars and forks, which indicate their adoption/acceptance by developers. Upon removing outliers with a one-class classifier, we employ Principal Feature Analysis and examine the semantics among metrics to provide an analysis on five axes for source code components (classes or packages): complexity, coupling, size, degree of inheritance, and quality of documentation. Neural networks are thus applied to estimate the final quality score given metrics from these axes. Preliminary evaluation indicates that our approach effectively estimates software quality at both class and package levels.}
}

2012

Inbooks

Kiriakos C. Chatzidimitriou, Ioannis Partalas, Pericles A. Mitkas and Ioannis Vlahavas
"Transferring Evolved Reservoir Features in Reinforcement Learning Tasks"
Charpter:1, 7188, pp. 213-224, Springer Berlin Heidelberg, 2012 Jan

Lecture Notes in Artificial Intelligent (LNAI)

@inbook{2012ChatzidimitriouLNAI,
author={Kiriakos C. Chatzidimitriou and Ioannis Partalas and Pericles A. Mitkas and Ioannis Vlahavas},
title={Transferring Evolved Reservoir Features in Reinforcement Learning Tasks},
chapter={1},
volume={7188},
pages={213-224},
publisher={Springer Berlin Heidelberg},
year={2012},
month={01},
date={2012-01-01},
url={http://issel.ee.auth.gr/wp-content/uploads/2017/01/Transferring-Evolved-Reservoir-Features-in-Reinforcement-Learning-Tasks.pdf},
doi={http://issel.ee.auth.gr/wp-content/uploads/publications/chp_LNAI.pdf},
abstract={Lecture Notes in Artificial Intelligent (LNAI)}
}

Andreas L. Symeonidis, Panagiotis Toulis and Pericles A. Mitkas
"Supporting Agent-Oriented Software Engineering for Data Mining Enhanced Agent Development"
Charpter:1, 7607, pp. 7-21, Springer Berlin Heidelberg, 2012 Jun

Lecture Notes in Computer Science

@inbook{2012SymeonidisLNCS,
author={Andreas L. Symeonidis and Panagiotis Toulis and Pericles A. Mitkas},
title={Supporting Agent-Oriented Software Engineering for Data Mining Enhanced Agent Development},
chapter={1},
volume={7607},
pages={7-21},
publisher={Springer Berlin Heidelberg},
year={2012},
month={06},
date={2012-06-04},
url={http://issel.ee.auth.gr/wp-content/uploads/2017/01/Supporting-Agent-Oriented-Software-Engineering-for-Data-Mining-Enhanced-Agent-Development-1.pdf},
abstract={Lecture Notes in Computer Science}
}