Using automatic static analysis to identify technical debt

Vetro,

doi:10.1109/icse.2012.6227226

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…These problems may impede the software maintenance process and impose the need for code refactoring (Fontana et al, 2012). In addition, Automatic Static Analysis (ASA) tools, such as FindBugs 14 or Checkstyle, 15 are also widely used to indicate TD (Vetro', 2012;Zazworka et al, 2014;Izurieta et al, 2012). ASA tools allow the analysis of source code in search for bugs or violations of good programming practices that can cause failures or quality decay of the software.…”

Section: Td Indicator Definitionmentioning

confidence: 99%

Technical debt forecasting: An empirical study on open-source repositories

Tsoukalas

Kehagias

Siavvas

et al. 2020

Journal of Systems and Software

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Section: Td Indicator Definitionmentioning

confidence: 99%

Technical debt forecasting: An empirical study on open-source repositories

Tsoukalas

Kehagias

Siavvas

et al. 2020

Journal of Systems and Software

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“…the absence of sufficient code tests) have been widely used to predict software maintainability decay and, in turn, the TD of a software application [25,29,43]. In addition, code issues that can negatively affect the quality of a software, such as code violations or bugs, have also been studied for their impact on TD [31,65,69]. Finally, code smells, i.e., sub-optimal design solutions that violate at least one programming principle [21] are considered in the literature as one of the first indications of the presence of TD [1,46].…”

Section: Datasetmentioning

confidence: 99%

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

et al. 2021

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Technical debt (TD) describes quality compromises that can yield short-term benefits but may negatively affect the quality of software products in the long run. A wide range of tools and techniques have been introduced over the years in order for the developers to be able to determine and manage TD. However, being able to also predict its future evolution is of equal importance to avoid its accumulation, and, in turn, the unlikely event of making the project unmaintainable. Although recent research endeavors have showcased the feasibility of building accurate project-specific TD forecasting models, there is a gap in the field regarding cross-project TD forecasting. Cross-project TD forecasting is of practical importance, since it would enable the application of pre-existing forecasting models on previously unknown software projects, especially new projects that do not exhibit sufficient commit history to enable the construction of project-specific models. To this end, in the present paper, we focus on cross-project TD forecasting, and we examine whether the consideration of similarities between software projects could be the key for more accurate forecasting. More specifically, we propose an approach based on data clustering. In fact, a relatively large repository of software projects is divided into clusters of similar projects with respect to their TD aspects, and specific TD forecasting models are built for each cluster, using regression algorithms. According to our approach, previously unknown software projects are assigned to one of the defined clusters and the cluster-specific TD forecasting model is applied to predict future TD values. The approach was evaluated through several experiments based on real-world applications. The results of the analysis suggest that the proposed approach comprises a promising solution for accurate cross-project TD forecasting.

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“…The defects can be further categorized to create prediction models in order to distribute the resolution resources more efficiently [12], [13], [14]. At the same time, efficiency, density, and principally the technical debt caused by defects, play critical roles as well [15].…”

Section: Related Workmentioning

confidence: 99%

Towards Automated Performance Bug Identification in Python

Tsakiltsidis,

Miranskyy,

Mazzawi

2016

Preprint

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Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement / marketing domain.Goal: Find a simple and easy to implement solution, predicting performance bugs.Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Naïve Bayes, Bayesian Networks, and Logistic Regression.Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall = 0.73, accuracy = 0.85, and precision = 0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection.Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs.

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Using automatic static analysis to identify technical debt

Cited by 7 publications

References 17 publications

Technical debt forecasting: An empirical study on open-source repositories

Technical debt forecasting: An empirical study on open-source repositories

A Clustering Approach Towards Cross-Project Technical Debt Forecasting

Towards Automated Performance Bug Identification in Python

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