اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدExplicit Programming Strategies
89
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Software developers solve a diverse and wide range of problems. While software engineering research often focuses on tools to support this problem solving, the strategies that developers use to solve problems are at least as important. In this paper, we offer a novel approach for enabling developers to follow explicit programming strategies that describe how an expert tackles a common programming problem. We define explicit programming strategies, grounding our definition in prior work both within software engineering and in other professions which have adopted more explicit procedures for problem solving. We then present a novel notation called Roboto and a novel StrategyTracker tool that explicitly represents programming strategies and frame executing strategies as a collaborative effort between human abilities to make decisions and computer abilities to structure process and persist information. Ina formative evaluation, 28 software developers of varying expertise completed a design task and a debugging task. We found that, compared to developers who are free to choose their strategies, developers gave explicit strategies experienced their work as more organized, systematic, and predictable, but also more constrained. Developers using explicit strategies were objectively more successful at the design and debugging tasks. We discuss the implications of Roboto and these findings, envisioning a thriving ecosystem of explicit strategies that accelerate and improve developers programming problem solving.
قيم البحث
اقرأ أيضاً
Ladder Logics is a programming language standardized in IEC 61131-3 and widely used for programming industrial Programmable Logic Controllers (PLC). A PLC program consists of inputs (whose values are given at runtime by factory sensors), outputs (who
se values are given at runtime to factory actuators), and the logical expressions computing output values from input values. Due to the graphical form of Ladder programs, and the amount of inputs and outputs in typical industrial programs, debugging such programs is time-consuming and error-prone. We present, in this paper, a Why3-based tool prototype we have implemented for automating the use of deductive verification in order to provide an easy-to-use and robust debugging tool for Ladder programmers.
Model learning has gained increasing interest in recent years. It derives behavioural models from test data of black-box systems. The main advantage offered by such techniques is that they enable model-based analysis without access to the internals o
f a system. Applications range from fully automated testing over model checking to system understanding. Current work focuses on learning variations of finite state machines. However, most techniques consider discrete time. In this paper, we present a method for learning timed automata, finite state machines extended with real-valued clocks. The learning method generates a model consistent with a set of timed traces collected by testing. This generation is based on genetic programming, a search-based technique for automatic program creation. We evaluate our approach on 44 timed systems, comprising four systems from the literature and 40 randomly generated examples.
Automated feedback generation for introductory programming assignments is useful for programming education. Most works try to generate feedback to correct a student program by comparing its behavior with an instructors reference program on selected t
ests. In this work, our aim is to generate verifiably correct program repairs as student feedback. The student assignment is aligned and composed with a reference solution in terms of control flow, and differences in data variables are automatically summarized via predicates to relate the variable names. Failed verification attempts for the equivalence of the two programs are exploited to obtain a collection of maxSMT queries, whose solutions point to repairs of the student assignment. We have conducted experiments on student assignments curated from a widely deployed intelligent tutoring system. Our results indicate that we can generate verified feedback in up to 58% of the assignments. More importantly, our system indicates when it is able to generate a verified feedback, which is then usable by novice students with high confidence.
In this paper, we propose an approach named psc2code to denoise the process of extracting source code from programming screencasts. First, psc2code leverages the Convolutional Neural Network based image classification to remove non-code and noisy-cod
e frames. Then, psc2code performs edge detection and clustering-based image segmentation to detect sub-windows in a code frame, and based on the detected sub-windows, it identifies and crops the screen region that is most likely to be a code editor. Finally, psc2code calls the API of a professional OCR tool to extract source code from the cropped code regions and leverages the OCRed cross-frame information in the programming screencast and the statistical language model of a large corpus of source code to correct errors in the OCRed source code. We conduct an experiment on 1,142 programming screencasts from YouTube. We find that our CNN-based image classification technique can effectively remove the non-code and noisy-code frames, which achieves an F1-score of 0.95 on the valid code frames. Based on the source code denoised by psc2code, we implement two applications: 1) a programming screencast search engine; 2) an interaction-enhanced programming screencast watching tool. Based on the source code extracted from the 1,142 collected programming screencasts, our experiments show that our programming screencast search engine achieves the precision@5, 10, and 20 of 0.93, 0.81, and 0.63, respectively.
It has been already shown that combinatorial evolution - the creation of new things through the combination of existing things - can be a powerful way to evolve rather than design technical objects such as electronic circuits in a computer simulation
. Most intriguingly, this seems to be an ongoing and thus open-ended process to create novelty with increasing complexity. In the present paper, we want to employ combinatorial evolution in software development. While current approaches such as genetic programming are efficient in solving particular problems, they all converge towards a solution and do not create anything new anymore afterwards. Combinatorial evolution of complex systems such as languages and technology are considered open-ended. Therefore, open-ended automatic programming might be possible through combinatorial evolution. Here, we implemented a computer program simulating combinatorial evolution of code blocks stored in a database to make them available for combining. Automatic programming is achieved by evaluating regular expressions. We found that reserved key words of a programming language are suitable for defining the basic code blocks at the beginning of the simulation. We also found that placeholders can be used to combine code blocks and that code complexity can be described in terms of the importance to the programming language. As in the previous combinatorial evolution simulation of electronic circuits, complexity increased from simple keywords and special characters to more complex variable declarations, to class definitions, to methods, and to classes containing methods and variable declarations. Combinatorial evolution, therefore, seems to be a promising approach for open-ended automatic programming.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
