Do Code Smells Impact the Effort of Different Maintenance Programming Activities?

Abstract

Abstract-Empirical studies have shown so far that code smells have relatively low impact over maintenance effort at file level. We surmise that previous studies have found low effects of code smells because the effort considered is a "sheer-effort" that does not distinguish between the kinds of developers' activities. In our study, we investigate the effects of code smells at the activity level. Examples of activities are: reading, editing, searching, and navigating, which are performed independently over different files during maintenance. We conjecture that structural attributes represented in the form of different code smells do indeed have an effect on the effort for performing certain kinds of activities. To verify this conjecture, we revisit a previous study about the impact of code smell on maintenance effort, using the same dataset, but considering activity effort. Six professional developers were hired to perform three maintenance tasks on four functionally equivalent Java Systems. Each developer performs two maintenance tasks. During maintenance task, we monitor developers' logs. Then, we define an annotation schema to identify developers' activities and assess whether code smells affect different maintenance activities. Results show that different code smells affect differently activity effort. Yet, the size of the changes performed to solve the task impacts the effort of all activities more than code smells and file size. While code smells impact the editing and navigating effort more than file size, the file size impacts the reading and searching activities more than code smells. One major implication of these results is that if code smells indeed affect the effort of certain kinds of activities, it means that their effects are contingent on the type of maintenance task at hand, where some kinds of activities will become more predominant than others.

Identifying and Prioritizing Architectural Debt Through Architectural Smells: A Case Study in a Large Software Company

“…An exploratory survey on code smells has been performed by Yamashita et al [9]: their results showed that a large proportion of developers did not know about code smells. Soh et al [10] conducted a study where professionals were hired to perform maintenance tasks on functionally equivalent Java projects in order to assess whether code smells a↵ect maintenance activities. Another empirical study on understanding maintenance problems related to code smells, has been done by Yamashita [11].…”

A survey on software smells

“…Maintainability [11], [100], [82], [137], [136], [138], [119] Effort/Cost [117], [120], [105], [68] Reliability [51], [47], [139], [12], [81], [56] Change proneness [88], [56], [139], [ A high number of smells (and hence high technical debt) negatively impact the morale and motivation of the development team and may lead to high attrition [125,123].…”

Towards Activity-Aware Tool Support for Change Tasks

“…[4]), all the way to very low-level activities, such as microtasks [16], activities for incremental change [17] or interactions with code elements (classes, methods and even lines) and commands used [7], [8], [18]- [22]. Soh et al [23] examine developers' efforts on such activities. Based on an extensive literature survey of empirical studies in the area, we came up with a coding of developer activities that is illustrated in Figure 1.…”

“…Editing code (adding, altering, removing) [2], [3], [7], [8], [12]- [15], [23] Editing test code [16], [19], [21] Editing functionality [16], [19] Write function description [16] Write call [16] Planning changes [3], [12], [13], [16] Understanding code [7], [13], [14], [23] Searching/locating/finding code and/or initial focus points [4], [14], [16], [17], [23] Searching for a string [2] Locating var definition/use [12] Navigating code dependencies and relating code [4], [7], [8], [23] Navigating static/indirect dependencies [2] Observing program execution [8], [12]- [15] Forming/investigating hypotheses, developing/answering questions [12], [13] Understanding a specific code element (Understanding subgraph [4])…”