Understanding Similar Code through Comparative Comprehension

Middleton, Justin; Stolee, Kathryn T.

doi:10.1109/vl/hcc53370.2022.9833117

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…To better understand the software, we can look for cases of similar code in the project in order to discover not obvious relations and analyze subtle differences between them [93].…”

Section: F Finding Code Similaritiesmentioning

confidence: 99%

Semantic Code Graph—An Information Model to Facilitate Software Comprehension

Borowski,

Balis,

Orzechowski

2024

IEEE Access

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Software comprehension can be extremely time-consuming due to the ever-growing size of codebases. Consequently, there is an increasing need to accelerate the code comprehension process to facilitate maintenance and reduce associated costs. A crucial aspect of this process is understanding and preserving the high quality of the code dependency structure. While a variety of code structure models already exist, there is a surprising lack of models that closely represent the source code and focus on software comprehension. As a result, there are no readily available and easy-to-use tools to assist with dependency comprehension, refactoring, and quality monitoring of code. To address this gap, we propose the Semantic Code Graph (SCG), an information model that offers a detailed abstract representation of code dependencies with a close relationship to the source code. We establish the critical properties of the SCG model and demonstrate its implementation for Java and Scala languages. To validate the SCG model's usefulness in software comprehension, we compare it to nine other source code representation models. Additionally, we select 11 well-known and widelyused open-source projects developed in Java and Scala and perform a range of software comprehension activities on them using three different code representation models: the proposed SCG, the Call Graph, and the Class Collaboration Network. We then qualitatively analyze the results to compare the performance of these models in terms of software comprehension capabilities. These activities encompass project structure comprehension, identifying critical project entities, interactive visualization of code dependencies, and uncovering code similarities through software mining. Our findings demonstrate that the SCG enhances software comprehension capabilities compared to the prevailing Class Collaboration Network and Call Graph models. Moreover, the SCG-based data analysis yields actionable software comprehension insights. We also release an open-source tool, scg-cli, to assist with result reproduction and further research. We believe that the work described is a step towards the next generation of tools that streamline code dependency comprehension and management.

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“…To better understand the software, we can look for cases of similar code in the project in order to discover not obvious relations and analyze subtle differences between them [93].…”

Section: F Finding Code Similaritiesmentioning

confidence: 99%

Semantic Code Graph—An Information Model to Facilitate Software Comprehension

Borowski,

Balis,

Orzechowski

2024

IEEE Access

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Barriers for Students During Code Change Comprehension

Middleton,

Ore,

Stolee

2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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A Model of How Students Engineer Test Cases With Feedback

Shin,

Kazerouni

2024

ACM Trans. Comput. Educ.

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Background and Context. Students’ programming projects are often assessed on the basis of their tests as well as their implementations, most commonly using test adequacy criteria like branch coverage, or, in some cases, mutation analysis. As a result, students are implicitly encouraged to use these tools during their development process (i.e., so they have awareness of the strength of their own test suites). Objectives. Little is known about how students choose test cases for their software while being guided by these feedback mechanisms. We aim to explore the interaction between students and commonly used testing feedback mechanisms (in this case, branch coverage and mutation-based feedback). Method. We use grounded theory to explore this interaction. We conducted 12 think-aloud interviews with students as they were asked to complete a series of software testing tasks, each of which involved a different feedback mechanism. Interviews were recorded and transcripts were analyzed, and we present the overarching themes that emerged from our analysis. Findings. Our findings are organized into a process model describing how students completed software testing tasks while being guided by a test adequacy criterion. Program comprehension strategies were commonly employed to reason about feedback and devise test cases. Mutation-based feedback tended to be cognitively overwhelming for students, and they resorted to weaker heuristics in order to address this feedback. Implications. In the presence of testing feedback, students did not appear to consider problem coverage as a testing goal so much as program coverage . While test adequacy criteria can be useful for assessment of software tests, we must consider whether they represent good goals for testing, and if our current methods of practice and assessment are encouraging poor testing habits.

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Understanding Similar Code through Comparative Comprehension

Cited by 3 publications

References 48 publications

Semantic Code Graph—An Information Model to Facilitate Software Comprehension

Semantic Code Graph—An Information Model to Facilitate Software Comprehension

Barriers for Students During Code Change Comprehension

A Model of How Students Engineer Test Cases With Feedback

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