Design debt prioritization

Kumar

J Software Evolu Process

2023

Code smells indicate poor design and imperfection in coding style. They increase maintenance costs and degrade software quality. Detecting and mitigating these code smells reduce the effects of flaws present in the software design. However, to save time and effort, it is a good practice to prioritize critical code smells and resolve them first. There have been various empirical studies on code smell prioritization in the last decades. Although the research community has been continuously working on the prioritization of code smells, yet a systematic mapping of all these studies would provide a panorama of research work. With this motivation, we selected 39 primary studies published between May 2011 and May 2022 and classified them based on seven factors: (i) code smells, (ii) factors, (iii) evaluation techniques, (iv) tools, (v) ranking formulas, (vi) data sets used, and (vii) measurements used for validating the research work. This paper aims to outline and discuss the prioritization procedure for code smells in object‐oriented software systems. The issues and recommendations for every question act as input for the researchers and industrialists for further research. Some issues for further investigation are less coverage of architecture smells, limited ranking formulas, lack of automated evaluation techniques, and so forth.

“…Code smells (or code anomalies or code technical debts) are design flaws that cause unnecessary complications in the source code 7,8 . In 1999, Fowler et al 9 introduced 22 code smells.…”

Section: Introductionmentioning

confidence: 99%

Code smell prioritization in object‐oriented software systems: A systematic literature review

Kumar

J Software Evolu Process

2023

J Software Evolu Process

“…Technical debt management (TDM) is the process of identifying, acknowledging, measuring, repaying, and maintaining TD 16 . Unfortunately, TDM is a very challenging process to conduct, even for well‐motivated and highly experienced individuals 17,18 . A naive approach to TDM would be attempting to repay all TD present in a given software system.…”

Section: Introductionmentioning

confidence: 99%

Technical debt (TD) through the lens of Twitter: A survey

Alfayez

Winn

Ding

et al. 2023

Technical debt (TD) is a metaphor used to refer to the added software system costs acquired from taking shortcuts. Unfortunately, large amounts of TD can lead to serious consequences, and, thus, the management of TD is essential. Due to TD being a relatively new subject of study, many aspects of TD remain ambiguous. Fortunately, Twitter has been proven to hold a wealth of information on many subjects. As such, this survey study aims to gain a better understanding on how interest in TD has evolved over time and how TD is addressed on Twitter. A total of 128,897 TD‐related tweets were scrapped from Twitter and analyzed using a number of proxy measures and Latent Dirichlet Allocation (LDA). The results revealed that interest in TD on Twitter has been generally increasing since the platform's early stages. Furthermore, TD‐related tweets were found to revolve around 11 distinct categories. The TD in games category was discovered to be the most popular category, followed by TD communication and TD repayment. The results highlight that TD is a diverse and overarching topic that contains many potential avenues for further exploration. Software engineering researchers, practitioners, and educators can utilize this study to help steer their TD‐related future efforts.

“…These papers use code metrics to quantify each technical debt item's principal and interest cost. This quantification is used with other techniques, such as cost-benefit ranking (Zazworka, Seaman, and Shull, 2011;Yli-Huumo et al, 2016;Plösch et al, 2018), Change Control Boards (CCB) (Snipes et al, 2012), and mathematical formulas (L. F. Ribeiro et al, 2017;Skourletopoulos, Mavromoustakis, et al, 2016) to prioritize the technical debt items. Data mining and historical analysis are also widely used techniques to prioritize technical debt (Falessi and Voegele, 2015;Choudhary and P. Singh, 2016;Mensah et al, 2018;Detofeno et al, 2022;Tsoukalas, Siavvas, et al, 2023;B.…”

Section: Classification Papersmentioning

confidence: 99%

“…These papers take advantage of the knowledge and methods developed within the finance area. They apply methods used in finance and business areas, such as the portfolio approach (Brauer et al, 2017;Plösch et al, 2018), and options (Fernández-Sánchez, Díaz, et al, 2014;Abad and Ruhe, 2015;Aldaeej and Seaman, 2018) to prioritize technical debt items. Some papers (De Almeida et al, 2021;Da Silva et al, 2022;M.…”

Section: Classification Papersmentioning

confidence: 99%

“…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).…”

Section: Classification Papersmentioning

confidence: 99%

See 1 more Smart Citation

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

Pina