JCaliper

Abstract

Technical Debt (TD) reflects problems in software maintainability along evolution. TD principal is defined as the effort required for refactoring an existing system to an ideal one (a.k.a. optimal) that suffers from no maintainability problems. One of the open problems in the TD community is that ideal versions of systems do not exist, and there are no methods in the literature for approaching them, even theoretically. To alleviate this problem, in this paper we propose an efficient TD management strategy, by applying Search-Based Software Engineering techniques. In particular, we focus on one specific aspect of TD, namely inefficient software modularity, by properly assigning behavior and state to classes through search space exploration. At the same time, in the context of TD, we: (a) investigate the use of local search algorithms to obtain a near-optimum solution and propose TD repayment actions (i.e., refactorings), and (b) calculate the distance of a design to the corresponding optimal (i.e., a proxy of TD principal). The approach has been implemented in the JCaliper Eclipse plugin enabling a case study, which validates the approach and contrasts it to existing measure of software evolution. CCS CONCEPTSSoftware and its engineering → Software creation and management → {Software development techniques → Objectoriented development, Software verification and validation → Empirical software validation}

Technical debt prioritization: methods, techniques, and a large exploratory study

“…Framework (Leppanen et al, 2015), (Fernández-Sánchez, Garbajosa, et al, 2015), (Martini, Bosch, and Chaudron, 2015), (Martini and Bosch, 2016), (Brauer et al, 2017), (Martini and Bosch, 2017), (Hormann et al, 2017), (R. d. Almeida et al, 2018), (R. R. d. , , (M. G. Stochel, Cholda, et al, 2020), (S. Freire, Rios, Gutierrez, et al, 2020), (Mandic et al, 2021), (Wiese et al, 2022) Artificial Intelligence (Akbarinasaji, 2015), (Codabux and Williams, 2016), (Mohan et al, 2016), (Kouros et al, 2019), (Alfayez and Boehm, 2019) Cost-Benefit (Zazworka, Seaman, and Shull, 2011), (Snipes et al, 2012), (Skourletopoulos, Mavromoustakis, et al, 2016), (Yli-Huumo et al, 2016), (L. F. Ribeiro et al, 2017), (Plösch et al, 2018) Historical (Falessi and Voegele, 2015), (Choudhary and P. Singh, 2016), (Charalampidou et al, 2017), (Tornhill, 2018) Portfolio Approach (Yuepu , (Plösch et al, 2018) (Brauer et al, 2017), (Albarak and Bahsoon, 2018) Options (Fernández-Sánchez, Díaz, et al, 2014), (Abad and Ruhe, 2015), (Aldaeej and Seaman, 2018) The papers discuss ways to standardize the technical debt prioritization process in the Framework category. Most of the frameworks developed consist in finding technical debt items, measuring their payment cost and interest (Fernández-Sánchez, Garbajosa, et al, 2015) (Leppanen et al, 2015) (Martini and Bosch, 2016).…”

“…Besides that, machine learning algorithms are also used to train predictors to infer technical debt prioritization (Akbarinasaji, 2015) (Codabux and Williams, 2016). Search-based methods were also used to prioritize the technical debt items (Alfayez and Boehm, 2019;Kouros et al, 2019).…”