Mining Developers’ Workflows from IDE Usage

Ioannou, Constantina; Burattin, Andrea; Weber, Barbara

doi:10.1007/978-3-319-92898-2_14

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…A decade ago, [51] mined software repositories to extract knowledge about the underlying software processes, and [52,53] have learned about user behavior from software at operations. [54] was able to extract events from Eclipse and have discovered, using a process mining tool, basic developers' workflows. Some statistics were computed based on the activities executed and artifacts edited.…”

Section: Related Workmentioning

confidence: 99%

Unveiling process insights from refactoring practices

Caldeira,

Abreu,

Cardoso

et al. 2020

Preprint

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Context: Software comprehension and maintenance activities, such as refactoring, are said to be negatively impacted by software complexity. The methods used to measure software product and processes complexity have been thoroughly debated in the literature. However, the discernment about the possible links between these two dimensions, particularly on the benefits of using the process perspective, has a long journey ahead. Objective: To improve the understanding of the liaison of developers' activities and software complexity within a refactoring task, namely by evaluating if process metrics gathered from the IDE, using process mining methods and tools, are suitable to accurately classify different refactoring practices and the resulting software complexity. Method : We mined source code metrics from a software product after a quality improvement task was given in parallel to (117) software developers, organized in (71) teams. Simultaneously, we collected events from their IDE work sessions (320) and used process mining to model their processes and extract the correspondent metrics. Results: Most teams using a plugin for refactoring (JDeodorant) reduced software complexity more effectively and with simpler processes than the ones that performed refactoring using only Eclipse native features. We were able to find moderate correlations (≈43%) between software cyclomatic complexity and process cyclomatic complexity. Using only process driven metrics, we computed ≈30,000 models aiming to predict the type of refactoring method

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Section: Related Workmentioning

confidence: 99%

Unveiling process insights from refactoring practices

Caldeira,

Abreu,

Cardoso

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Going back almost a decade, [165] have mined software repositories to extract knowledge about the underlying software processes, and [180,181] have learned about user behavior from software at runtime. Recently, [96] was able to extract events from Eclipse and have discovered, using a process mining tool, basic developers' workflows. Some statistics were computed based on the activities executed and artifacts edited.…”

Section: Related Workmentioning

confidence: 99%

“…However, to the best of our knowledge, none of these works combine data from the IDE utilization with process mining methods with the aim of measuring individuals or teams efficiency. Even in the case of [96], where the approach is similar to ours, nothing was done related to conformance checking on the processes followed by developers, as there was no existing reference model to compare with. Our work introduces a valid approach for this purpose, and bring a new perspective to software development analytics by filling this gap.…”

Section: Related Workmentioning

confidence: 99%

Software Development Process Mining: Discovery, Conformance Checking and Enhancement

Caldeira

Abreu

2016

2016 10th International Conference on the Quality of Information and Communications Technology (QUATIC)

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Context. Modern software projects require the proper allocation of human, technical and financial resources. Very often, project managers make decisions supported only by their personal experience, intuition or simply by mirroring activities performed by others in similar contexts. Most attempts to avoid such practices use models based on lines of code, cyclomatic complexity or effort estimators, thus commonly supported by software repositories which are known to contain several flaws.Objective. Demonstrate the usefulness of process data and mining methods to enhance the software development practices, by assessing efficiency and unveil unknown process insights, thus contributing to the creation of novel models within the software development analytics realm.Method. We mined the development process fragments of multiple developers in three different scenarios by collecting Integrated Development Environment (IDE) events during their development sessions. Furthermore, we used process and text mining to discovery developers' workflows and their fingerprints, respectively.Results. We discovered and modeled with good quality developers' processes during programming sessions based on events extracted from their IDEs. We unveiled insights from coding practices in distinct refactoring tasks, built accurate software complexity forecast models based only on process metrics and setup a method for characterizing coherently developers' behaviors. The latter may ultimately lead to the creation of a catalog of software development process smells.Conclusions. Our approach is agnostic to programming languages, geographic location or development practices, making it suitable for challenging contexts such as in modern global software development projects using either traditional IDEs or sophisticated low/no code platforms.

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“…One of the main concerns in the software engineering discipline is to improve developers' productivity, increase product quality, and reduce the cost of developing software [5,45]. Different software development methods (e.g., Agile and Lean methods) [2], principles and practices (e.g., refactoring and test-driven development) [33], and tools (e.g., Integrated Development Environments or IDEs) have been put forth as means to address the aforementioned concerns [18]. In addition, in recent software engineering research, human aspects have obtained increasing attention as an important factor to improve productivity [14,27].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, we demonstrated the potential of using process mining to understand developers' interactions with an IDE during a programming task [18]. Using process mining we were able to identify different patterns of how developers solved programming tasks (i.e., top-down versus bottom-up approach).…”

Section: Introductionmentioning

confidence: 99%

Mining reading patterns from eye-tracking data: method and demonstration

et al. 2019

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Understanding how developers interact with different software artifacts when performing comprehension tasks has a potential to improve developers' productivity. In this paper, we propose a method to analyze eye-tracking data using process mining to find distinct reading patterns of how developers interacted with the different artifacts. To validate our approach, we conducted an exploratory study using eyetracking involving 11 participants. We applied our method to investigate how developers interact with different artifacts during domain and code understanding tasks. To contextualize the reading patterns and to better understand the perceived benefits and challenges participants associated with the different artifacts and their choice of reading patterns, we complemented the eye-tracking data with the data obtained from think aloud. The study used behavior driven development (BDD), a development practice that is increasingly used in Agile software development contexts, as a setting. The study shows that our method can be used to explore developers' behavior at an aggregated level and identify behavioral patterns at varying levels of granularity.

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Mining Developers’ Workflows from IDE Usage

Cited by 11 publications

References 20 publications

Unveiling process insights from refactoring practices

Unveiling process insights from refactoring practices

Software Development Process Mining: Discovery, Conformance Checking and Enhancement

Mining reading patterns from eye-tracking data: method and demonstration

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