Exploratory Experimental Studies Comparing Online and Offline Programing Performance

Sackman, H.; Erikson, W. J.; Grant, E. E.

doi:10.21236/ad0645438

Cited by 5 publications

(3 citation statements)

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“…Since human software developers keep playing a central role in every software project and directly impact its success, this relatively new type of expertise is attracting increasing attention from modern industries 1, 2 and educations 3,4 . Moreover, software engineering researchers repeatedly found huge variations in productivity even between programmers with the same level of experience [5][6][7] . Several previous studies showed the psychological characteristics of expert programmers in their behaviors 8,9 , knowledge structures [10][11][12] , and eye movements [13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

Expert programmers have fine-tuned cortical representations of source code

Ikutani

Kubo

Nishida

et al. 2020

Preprint

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Expertise enables humans to achieve outstanding performance on domain-specific tasks, and programming is no exception. Many have shown that expert programmers exhibit remarkable differences from novices in behavioral performance, knowledge structure, and selective attention. However, the underlying differences in the brain are still unclear. We here address this issue by associating the cortical representation of source code with individual programming expertise using a data-driven decoding approach. This approach enabled us to identify seven brain regions, widely distributed in the frontal, parietal, and temporal cortices, that have a tight relationship with programming expertise. In these brain regions, functional categories of source code could be decoded from brain activity and the decoding accuracies were significantly correlated with individual behavioral performances on source-code categorization. Our results suggest that programming expertise is built up on fine-tuned cortical representations specialized for the domain of programming.

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Section: Introductionmentioning

confidence: 99%

Expert programmers have fine-tuned cortical representations of source code

Ikutani

Kubo

Nishida

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The core of this method is to extract program semantics from the code quickly and automatically, establish the relationship between program implementation and requirements, and help developers understand the program for requirements validation. Previous research work on program understanding [8] [9] is mainly based on program slicing, program labeling and execution visualization technology.…”

Section: Introductionmentioning

confidence: 99%

Program Understanding and Requirement Validation Based on Accurate Value Flow Graph

2020

Proceedings of 2020 the 10th International Workshop on Computer Science and Engineering

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In software development, testing, verification and maintenance, it is a significant challenge to guarantee the consistency between software system and requirement. Software logic is usually implemented in the form of source code while requirements are usually described in the form of (structured) text. There are significant differences in form and connoation between the two, which brings many difficulties to software development. To solve this problem, this paper proposes a program analysis and requirement extraction algorithm based on accurate value flow graph. The algorithm uses static analysis to analyze program semantics and construct accurate value flow graphs. On this basis, the semantic information is extracted and summary is generated, which can be further used for requirements confirmation and defect analysis. Experiments show that the algorithm can analyze the control and data dependence of software code, and can accurately generate variable association information under different paths. The function summary can help developers to confirm whether the program semantics are consistent with the software requirements, help to find subtle semantic differences (including differences in conditions, dependencies, etc.), and effectively improve the efficiency of program understanding (up to 60% of time can be saved). This tool can be further used in the fields of defect analysis, influence domain analysis, etc., and has good application value.

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“…Indeed, experienced software engineers generally benefit from this kind of expertise [Adel85]. Other studies indicate large differences in the abilities of different software developers [Sackm68,Curt81], and a number of techniques have been proposed to take the best advantage of the more experienced software engineers [Brook78].…”

Section: αι-Based Approaches For Software Reusementioning

confidence: 99%

Employing Genericity and Case-Based Reasoning to Effectively Reuse Code

Καταλαγαριανοσ¹

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Systematic reuse of code has been proposed as a promising means to address the legendary productivity increase in software development. While object-oriented programming languages are, by nature, well suited for reusability-based development of applications, additional mechanisms to effectively reuse code are necessary.Effective reuse of code requires a rich collection of designed-for-reuse software components and knowledge on how to locate them in a repository, adapt them if needed, and even create new ones based on information provided by other components exploiting similar characteristics.The application of techniques and methods from artificial intelligence to software engineering is one mechanism through which reusability of software might be achieved. By abstracting and encoding the expertise of experienced software engineers into knowledge bases together with software components, system developers can gain effective access to the artifacts in the software repository as it evolves over time.Under this perspective, a novel method is presented. The method uses genericity (a technique that allows a module to be defined with parameterized types), and employs a special form of Case-Based Reasoning (a method of solving problems based on the transfer of past experience to new problem situations) in order to make object-oriented code reusable.Using established correspondences (links) and experiences from previous situations, the system semi-automatically finds a good match (e.g., a class) in the repository to the user's requirements, possibly adapting it to specific needs. Adaptation is based on specifying type parameters while making the necessary method modifications. This process is also reversible (generic classes from specific ones). Finally, the repository is enriched with the new component(s) and process knowledge, followed by an automatic appropriate reorganization.The method presented in this thesis has been evaluated through a prototype implementation, which addresses the reuse of C++ code. The prototype system runs on SparcStations and Sun 4 series under Unix. The language used for the implementation is a Prolog-like language called MegaLog. Additionally, a usage experiment performed in order to get an indication on the usage characteristics of the prototype system.The proposed method integrates ideas and techniques from various fields like knowledge representation, software engineering and machine learning. It addresses only technical issues for the effective reuse problem. To be practical, reuse must address not only technical but managerial, economic, legal, cultural, and technology transfer issues.In considering the application of the method in practical situations several operational characteristics need to be resolved, like synonym handling, the provision of an undo operation, etc.Future research could include a study on the extensibility of our method for reusing applications, not just code. Concerning the retrieval of similar past cases and their adaptation, additional types of similarity should ...

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Exploratory Experimental Studies Comparing Online and Offline Programing Performance

Cited by 5 publications

References 1 publication

Expert programmers have fine-tuned cortical representations of source code

Expert programmers have fine-tuned cortical representations of source code

Program Understanding and Requirement Validation Based on Accurate Value Flow Graph

Employing Genericity and Case-Based Reasoning to Effectively Reuse Code

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