Eliminating Software Caused Mission Failures

Dorin, Michael; Montenegro, Sérgio

doi:10.1109/aero.2019.8741837

Cited by 2 publications

(4 citation statements)

References 4 publications

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“…The original research for XP'18 was focused on the ascetic nature of the source code with no consideration of language syntax and semantics [14]. A summary of the results is shown in Table 1.…”

Section: Existing Data: Programmer Surveymentioning

confidence: 99%

Linguistic Economy Applied to Programming Language Identifiers

Dorin¹,

Montenegro²

2021

JSEA

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Though many different readability metrics have been created, there still is no universal agreement defining readability of software source code. The lack of a clear agreement of source code readability has ramifications in many areas of the software development life-cycle, not least of which being software maintainability. We propose a measurement based on Linguistic Economy to bridge the gap between mathematical and behavioral aspects. Linguistic Economy describes efficiencies of speech and is generally applied to natural languages. In our study, we create a large corpus of words that are likely to be found in a programmer's vocabulary, and a corpus of existing identifiers found in a collection of open-source projects. We perform a usage analysis to create a database from both of these corpora. Linguistic Economy suggests that words requiring less effort to speak are used more often than words requiring more effort. This concept is applied to measure how difficult program identifiers are to understand by extracting them from the program source and comparing their usage to the database. Through this process, we can identify source code that programmers find difficult to review. We validate our work using data from a survey where programmers identified unpleasant to review source files. The results indicate that source files identified as unpleasant to review source code have more linguistically complicated identifiers than pleasant programs.

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Section: Existing Data: Programmer Surveymentioning

confidence: 99%

Linguistic Economy Applied to Programming Language Identifiers

Dorin¹,

Montenegro²

2021

JSEA

Self Cite

View full text Add to dashboard Cite

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“…An even more applicable subject is image processing and sentiment analysis. Qian et al analysed twitter messages attempting to capture human expressiveness with image recognition [7]. Zhang et al (2015) describe microblogs' sentiment analysis by integrating text and image features [8].…”

Section: Machine Learning and Image Analysismentioning

confidence: 99%

“…This paper uses some data originally gathered in preparation for the 2019 IEEE Aerospace Conference in Big Sky, Montana (Aeroconf). At Aeroconf, we wanted to study what stylistic issues were most problematic for code reviews [7]. For this study, we created 'code snippets' and asked programmers to determine the proper outcome should the code be executed.…”

Section: Data From Previous Workmentioning

confidence: 99%

“…Our hypothetical project was given characteristics based off these numbers and is shown in Table 2 with the results. We used the results of the Aeroconf [7] study to estimate how long code with and without tumours takes to review, and in our hypothetical project the presence of tumours increased the review time by 21%. It is also important to note that not only does the presence of tumours increase the review time, but it also reduces the accuracy of the review to lower than 90% [7].…”

Section: Impact Of Tumoursmentioning

confidence: 99%

See 1 more Smart Citation

Using Machine Learning Image Recognition for Code Reviews

Dorin¹,

Le²,

Kolakaluri³

et al. 2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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It is commonly understood that code reviews are a cost-effective way of finding faults early in the development cycle. However, many modern software developers are too busy to do them. Skipping code reviews means a loss of opportunity to detect expensive faults prior to software release. Software engineers can be pushed in many directions and reviewing code is very often considered an undesirable task, especially when time is wasted reviewing programs that are not ready. In this study, we wish to ascertain the potential for using machine learning and image recognition to detect immature software source code prior to a review. We show that it is possible to use machine learning to detect software problems visually and allow code reviews to focus on application details. The results are promising and are an indication that further research could be valuable.

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Eliminating Software Caused Mission Failures

Cited by 2 publications

References 4 publications

Linguistic Economy Applied to Programming Language Identifiers

Linguistic Economy Applied to Programming Language Identifiers

Using Machine Learning Image Recognition for Code Reviews

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