A large scale empirical study of the impact of Spaghetti Code and Blob anti-patterns on program comprehension

Abstract

Context: Several studies investigated the impact of anti-patterns (i.e., "poor" solutions to recurring design problems) during maintenance activities and reported that anti-patterns significantly affect the developers' effort required to edit files. However, before developers edit files, they must understand the source code of the systems. This source code must be easy to understand by developers. Objective: In this work, we provide a complete assessment of the impact of two instances of two anti-patterns, Blob or Spaghetti Code, on program comprehension. Method: We analyze the impact of these two anti-patterns through three empirical studies conducted at Polytechnique Montréal (Canada) with 24 participants; at Carlton University (Canada) with 30 participants; and at University Basilicata (Italy) with 79 participants. Results: We collect data from 372 tasks obtained thanks to 133 different participants from the three universities. We use three metrics to assess the developers' comprehension of the source code: (1) the duration to complete each task; (2) their percentage of correct answers; and, (3) the NASA task load index for their effort. Conclusions: We report that, although single occurrences of Blob or Spaghetti code anti-patterns have little effect on code comprehension, two occurrences of either Blob or Spaghetti Code significantly increases the developers' time spent in their tasks, reduce their percentage of correct answers, and increase their effort. Hence, we recommend that developers act on both anti-patterns, which should be refactored out of the source code whenever possible. We also recommend further studies on combinations of anti-patterns rather than on single anti-patterns one at a time.

Are Multi-language Design Smells Fault-prone? An Empirical Study

“…They concluded that the occurrence of one anti-pattern does not significantly impacts comprehension while the combination of the two anti-patterns negatively impact program comprehension. This finding was corroborated by Politowski et al [69]. Linares et al [70] studied the potential relationship between the occurrence of design smells and quality attributes as well as the possible relation between design smells and application domains.…”

Code Red: The Business Impact of Code Quality -- A Quantitative Study of 39 Proprietary Production Codebases

“…Recent research indicates that, on average, developers spend about 60% of their time on program comprehension activities [63]. In this light, the quality of the existing code plays a significant role: a study by Politowski et al shows that code containing the anti-pattern Spaghetti Code is 39.5% more time consuming to understand than code without this particular code smell [49]. All together, we identified the need for a more comprehensive metric that includes code smell detectionthus we turned our attention to CodeScene's Code Health.…”

Investigando o Impacto das Coocorrências de Code Smells nos Atributos Internos de Qualidade

“…É possível perceber na Tabela 3, que nenhum dos trabalhos relacionados não verifica quais as coocorrências mais prejudiciais para atributos internos de qualidade, a maioria leva em considerac ¸ão apenas projetos open source. Somente um estudo [Politowski et al 2020], leva em considerac ¸ão a percepc ¸ão dos desenvolvedores sobre a refatorac ¸ão de coocorrências de code smells. Dessa forma, as principais necessidades são: (i) desenvolver mais estudos de refatorac ¸ão no contexto de sistemas industriais; (ii) analisar a percepc ¸ão dos desenvolvedores sobre as atividades de refatorac ¸ão de smells e coocorrências de code smells; e (iii) verificar o real impacto das coocorrências de code smells nos atributos internos de qualidade.…”

“…Alguns trabalhos têm avaliado não só as ocorrências individuais de code smells, mas sim as relac ¸ões entre essas anomalias e o impacto que elas causam para a qualidade de software [Yamashita et al 2015, Oizumi et al 2016, Politowski et al 2020. A presenc ¸a de ocorrências individuais de code smells não afeta de forma significante a compreensão de software e nem a performance dos desenvolvedores.…”