A theory of dual channel constraints

Casalnuovo, Casey; Barr, Earl T.; Dash, Santanu Kumar; Dévanbu, Prémkumar; Morgan, Emily

doi:10.1145/3377816.3381720

Cited by 24 publications

(19 citation statements)

References 13 publications

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“…the natural language identifiers that are chosen and the comments that are written in the code. Based on this insight, Casalnuovo et al [2020] described two communication channels in source code: the algorithmic channel (AL) and the natural language channel (NL). The algorithmic channel comprises of all the instructions written by the developers which will be executed by a computer.…”

Section: Related Workmentioning

confidence: 99%

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Do Names Echo Semantics? A Large-Scale Study of Identifiers Used in C++'s Named Casts

Petrescu¹,

Smith²,

Rafail³

et al. 2021

Preprint

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Developers relax restrictions on a type to reuse methods with other types. While type casts are prevalent, in weakly typed languages such as C++, they are also extremely permissive. If type conversions are performed without care, they can lead to software bugs. Therefore, there is a clear need to check whether a type conversion is essential and used adequately according to the developer's intent. In this paper, we propose a technique to judge the fidelity of type conversions from an explicit cast operation, using the identifiers in an assignment. We measure accord in the identifiers using entropy and use it to check if the semantics of the source expression in the cast match the semantics of the variable it is being assigned. We present the results of running our tool on 34 components of the Chromium project, which collectively account for 27MLOC. Our tool identified 1,368 cases of discord indicating potential anti-patterns in the usage of explicit casts. We performed a manual evaluation of a random-uniform sample of these cases. Our evaluation shows that our tool identified 25.6% cases representing incorrect implementations of named casts and 28.04% cases representing imprecise names of identifiers.

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Section: Related Workmentioning

confidence: 99%

“…In this paper, we propose a lightweight approach to check if casts are used judiciously. Casalnuovo et al [2020] presented source code as being dual channel. One channel is represented by the algorithmic channel, comprised of instructions understood and executed by computers.…”

Section: Introductionmentioning

confidence: 99%

Do Names Echo Semantics? A Large-Scale Study of Identifiers Used in C++'s Named Casts

Petrescu¹,

Smith²,

Rafail³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The theory of dual channel constraints (Casalnuovo, Barr, Dash, Devanbu, & Morgan, 2020) describes how programmers express meaning in code to each audience. In this formulation, code is comprised of two channels-an algorithmic (AL) channel and a natural language (NL) channel.…”

Section: Dual Channel Constraints and Equivalent Meaning In Codementioning

confidence: 99%

Do Programmers Prefer Predictable Expressions in Code?

et al. 2020

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Source code is a form of human communication, albeit one where the information shared between the programmers reading and writing the code is constrained by the requirement that the code executes correctly. Programming languages are more syntactically constrained than natural languages, but they are also very expressive, allowing a great many different ways to express even very simple computations. Still, code written by developers is highly predictable, and many programming tools have taken advantage of this phenomenon, relying on language model surprisal as a guiding mechanism. While surprisal has been validated as a measure of cognitive load in natural language, its relation to human cognitive processes in code is still poorly understood. In this paper, we explore the relationship between surprisal and programmer preference at a small granularity—do programmers prefer more predictable expressions in code? Using meaning‐preserving transformations, we produce equivalent alternatives to developer‐written code expressions and run a corpus study on Java and Python projects. In general, language models rate the code expressions developers choose to write as more predictable than these transformed alternatives. Then, we perform two human subject studies asking participants to choose between two equivalent snippets of Java code with different surprisal scores (one original and transformed). We find that programmers do prefer more predictable variants, and that stronger language models like the transformer align more often and more consistently with these preferences.

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“…Seshat can predict the entire kill matrix 2 that results from mutation analysis. We refer to this new type of predictive modelling as Predictive Mutation Analysis (PMA), to emphasise the finer granularity of the prediction, as opposed to PMT, whose outcome is the test suite level mutation analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Seshat exploits the Natural Language (NL) channel in software [2]. Natural Language channel refers to the communication channel that explains the conceptual contexts of the actual executions via natural language elements in source code, such as identifier names.…”

Section: Introductionmentioning

confidence: 99%

Predictive Mutation Analysis via Natural Language Channel in Source Code

Kim,

Jeon,

Hong

et al. 2021

Preprint

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Mutation analysis can provide valuable insights into both SystemUnder Test (SUT) and its test suite. However, it is not scalable due to the cost of building and testing a large number of mutants. Predictive Mutation Testing (PMT) has been proposed to reduce the cost of mutation testing, but it can only provide statistical inference about whether a mutant will be killed or not by the entire test suite. We propose Seshat, a Predictive Mutation Analysis (PMA) technique that can accurately predict the entire kill matrix, not just the mutation score of the given test suite. Seshat exploits the natural language channel in code, and learns the relationship between the syntactic and semantic concepts of each test case and the mutants it can kill, from a given kill matrix. The learnt model can later be used to predict the kill matrices for subsequent versions of the program, even after both the source and test code have changed significantly. Empirical evaluation using the programs in the Defects4J shows that Seshat can predict kill matrices with the average F-score of 0.83 for versions that are up to years apart. This is an improvement of F-score by 0.14 and 0.45 point over the state-of-the-art predictive mutation testing technique, and a simple coverage based heuristic, respectively. Seshat also performs as well as PMT for the prediction of the mutation score only. Once Seshat trains its model using a concrete mutation analysis, the subsequent predictions made by Seshat are on average 39 times faster than actual test-based analysis. CCS CONCEPTS• Software and its engineering → Software creation and management; • Computing methodologies → Machine learning.

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A theory of dual channel constraints

Cited by 24 publications

References 13 publications

Do Names Echo Semantics? A Large-Scale Study of Identifiers Used in C++'s Named Casts

Do Names Echo Semantics? A Large-Scale Study of Identifiers Used in C++'s Named Casts

Do Programmers Prefer Predictable Expressions in Code?

Predictive Mutation Analysis via Natural Language Channel in Source Code

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