Journal of Computer Languages最新文献

{"title":"Investigating the energy consumption of C++ and Java solutions mined from a programming contest site","authors":"Sérgio Queiroz de Medeiros,&nbsp;Marcelo Borges Nogueira,&nbsp;Gustavo Quezado","doi":"10.1016/j.cola.2025.101341","DOIUrl":"10.1016/j.cola.2025.101341","url":null,"abstract":"<div><div>The current concern about global warming has led to an increasing interest in the energy efficiency of computer applications. Assuming power is constant, the general trend is that faster programs consume less energy, thus optimizing a program for speed would also improve its energy efficiency.</div><div>We investigate this tendency in a set of C++ and Java solutions mined from Code Submission Evaluation System (CSES), a popular programming competition site, where each solution must give the correct answer under a given time limit. In such context, we can consider that all correct solutions for a problem were written with a speed concern, but not with energy efficiency in mind.</div><div>We selected 15 problems from CSES and for each of them we mined at least 30 C++ and Java solutions, evaluating time and energy efficiency of each solution in at least two different machines. In our scenario, where there is a great diversity of programming styles, execution speed, and memory usage, we could confirm the general trend: faster programs consume less energy. Moreover, we were able to use ordinary least squares to fit a linear function, with good precision, that relates energy consumption of a program to its execution time, as well as to automatically identify programs with abnormal energy consumption. A manual analysis of these programs revealed that often they perform a different amount of allocation and deallocation operations when compared to programs with similar execution times.</div><div>We also calculated the energy consumption profile of sets of random C++ solutions for these 15 CSES problems, and we tried to associate each set with its corresponding CSES problem by using the energy consumption profiles previously computed for each one of them. By using this approach, we could restrict, for each set of random C++ solutions, the classification task to a subset of 7 CSES problems, a reduction of more than 50% in the search space.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101341"},"PeriodicalIF":1.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Python’s evolution on Stack Overflow: An empirical analysis of topic trends","authors":"Fengqi Hu,&nbsp;Weihao Xue,&nbsp;Siyuan Zhou,&nbsp;Ye Wang,&nbsp;Bo Jiang,&nbsp;Qiao Huang,&nbsp;Hua Zhang","doi":"10.1016/j.cola.2025.101340","DOIUrl":"10.1016/j.cola.2025.101340","url":null,"abstract":"<div><div>With the rapid development of information technology and changing programming practices, the demand for programming discussions on online Q&amp;A platforms is growing. This study analyzes over two million Python-related posts on Stack Overflow to identify core topics and challenges over fifteen years. By using a Gradient Boosting Decision Tree (GBDT) model to quantify post popularity, we objectively show what the hottest as well as the most disturbing topics related to Python are to users at different times. We find that: The domains most closely associated with Python are data processing and machine learning, while development environments as well as automation and testing are gradually increasing in popularity. Machine learning is the area that bothers users the most. Moreover, we found that some questions that confuse users can increase the popularity of related topics. These findings can help developers grasp the direction of the Python language so that they can better plan their personal learning and project development. Enterprises and organizations can also optimize resource allocation based on trends in hot topics for training, tool development, and technical support.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101340"},"PeriodicalIF":1.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A methodology for empirical complexity analysis based on Newton’s polynomial interpolation","authors":"Rafael Fontes Sumitani,&nbsp;Lucas Victor da Silva Costa,&nbsp;Frederico F. Campos,&nbsp;Fernando Magno Quintão Pereira","doi":"10.1016/j.cola.2025.101347","DOIUrl":"10.1016/j.cola.2025.101347","url":null,"abstract":"<div><div>A cost model is a function that relates how often each part of a program runs depending on its inputs. Cost models can be derived automatically via the observation of counters: instrumentation that tracks execution of program operations. This paper defines Newton Counters: counters that can be described via a polynomial ranging on a single program input variable whose value can be read in constant time. Additionally, it shows that Newton Counters are prevalent in actual codes. Motivated by this observation, the paper introduces a methodology to derive automatic cost models. Said methodology combines static code analyses with interpolation via Newton’s divided difference method. This approach is currently available as a tool, <span>Merlin</span>. The effectiveness of this tool is demonstrated on 949 executable C programs taken from the <span>Jotai</span> collection, and on <span>genann</span>, a neural network library.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101347"},"PeriodicalIF":1.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mind the gap: The missing features of the tools to support user studies in software engineering","authors":"Lázaro Costa ,&nbsp;Susana Barbosa ,&nbsp;Jácome Cunha","doi":"10.1016/j.cola.2025.101345","DOIUrl":"10.1016/j.cola.2025.101345","url":null,"abstract":"<div><div>User studies are paramount for advancing research in software engineering, particularly when evaluating tools and techniques involving programmers. However, researchers face several barriers when performing them despite the existence of supporting tools. We base our study on a set of tools and researcher-reported barriers identified in prior work on user studies in software engineering. In this work, we study how existing tools and their features cope with previously identified barriers. Moreover, we propose new features for the barriers that lack support. We validated our proposal with 102 researchers, achieving statistically significant positive support for all but one feature. We study the current gap between tools and barriers, using features as the bridge. We show there is a significant lack of support for several barriers, as some have no single tool to support them.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101345"},"PeriodicalIF":1.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TestLoter: A logic-driven framework for automated unit test generation and error repair using large language models","authors":"Ruofan Yang,&nbsp;Xianghua Xu,&nbsp;Ran Wang","doi":"10.1016/j.cola.2025.101348","DOIUrl":"10.1016/j.cola.2025.101348","url":null,"abstract":"<div><div>Automated unit test generation is a critical technique for improving software quality and development efficiency. However, traditional methods often produce test cases with poor business consistency, while large language model based approaches face two major challenges: a high error rate in generated tests and insufficient code coverage. To address these issues, this paper proposes TestLoter, a logic-driven test generation framework. The core contributions of TestLoter are twofold. First, by integrating the structured analysis capabilities of white-box testing with the functional validation characteristics of black-box testing, we design a logic-driven test generation chain-of-thought that enables deep semantic analysis of code. Second, we establish a hierarchical repair mechanism to systematically correct errors in generated test cases, significantly enhancing the correctness of the test code. Experimental results on nine open-source projects covering various domains, such as data processing and utility libraries, demonstrate that TestLoter achieves 83.6% line coverage and 78% branch coverage. Our approach outperforms both LLM-based methods and traditional search-based software testing techniques in terms of coverage, while also reducing the number of errors in the generated unit test code.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101348"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144721370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating quantized Large Language Models for code generation on low-resource language benchmarks","authors":"Enkhbold Nyamsuren","doi":"10.1016/j.cola.2025.101351","DOIUrl":"10.1016/j.cola.2025.101351","url":null,"abstract":"<div><div>Democratization of AI, which is making AI accessible and usable for everyone, is an important topic with the broader topic of the digital divide. This issue is especially relevant to Large Language Models (LLM) that are becoming increasingly popular as AI co-pilots but suffer from a lack of accessibility due to high computational demand. In this study, we evaluate whether LLM quantization is a viable approach toward enabling LLMs on generic consumer devices. The study assesses the performance of five quantized code LLMs in Lua and Python code generation tasks. All code LLMs had approximately 7 billion parameters and were deployed on a generic CPU-only consumer laptop. To evaluate the impact of quantization, the models were tested at 2-, 4-, and 8-bit integer precisions. Pass@1 and pass@10 evaluations were done at variable temperatures and token sampling rates. Along with tasks such as question answering, text summarization, and text generation, programming tasks are one of the popular applications of AI co-pilots. Furthermore, code generation is a high-precision task, which makes it a suitable benchmark to evaluate and compare quantized models for everyday use by individuals. Lua is chosen as a low-resource language to avoid models’ biases related to high-resource languages. Performance in Lua is contrasted against performance in Python, which was chosen as a high-resource language. The results suggest that the models quantized at the 4-bit integer precision offer the best trade-off between performance and model size. These models can be comfortably deployed on an average laptop without a dedicated GPU. The findings suggest that lower quantization precision adversely affects more the performance in low-resource languages than in high-resource languages. But it also hinted that the quantization to an integer precision from the full precision affects more the performance in high-resource language. The quantized models at 8-bit integer precision require more inference that does not effectively translate to better performance. While quantization indeed increases the accessibility of smaller LLMs with 7 billion parameters, these LLMs demonstrate overall low performance (less than 50%) on high-precision and low-resource tasks such as Lua code generation. While accessibility is improved, usability is still not at a practical level of foundational LLMs such as GPT-4o or Llama 3.1 with 405 billion parameters. Additionally, in the most failed instances, the models excel at generating code that is free of syntax errors but fails at unit tests or has runtime issues. This means that any generated code requires extensive testing that may negate any potential efficiency boost delivered by these smaller coding models.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101351"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards democratisation of veterinary clinical protocols: Transferring their development from technical-coding experts to veterinary professionals for the case of Chronic Kidney Disease for Cats (CKD4Cats Domain-Specific Language)","authors":"Sofia Meacham ,&nbsp;Hessa Alfraihi","doi":"10.1016/j.cola.2025.101328","DOIUrl":"10.1016/j.cola.2025.101328","url":null,"abstract":"<div><div>This paper presents CKD4Cats, a domain-specific language (DSL) for computerised Chronic Kidney Disease (CKD) clinical protocols in cats - a very common disease in veterinary practice. Building on DSLs used in human health, CKD4Cats addresses veterinary-specific needs while addressing their shortcomings. Developed with JetBrains’ Meta-Programming System (MPS) and veterinary input, the DSL ensures ease of use and adoption. It employs advanced evaluation methods, creating a projectional editor that streamlines protocol creation, displays relevant options, and guarantees ”correct-by-construction” clinical protocols. This innovative approach democratises software development, making advanced tools accessible to non-technical users and significantly improving veterinary practice management.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"84 ","pages":"Article 101328"},"PeriodicalIF":1.7,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144177731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MLAPW: A framework to assess the impact of feature selection and sampling techniques on anti-pattern prediction using WSDL metrics","authors":"Lov Kumar ,&nbsp;Vikram Singh ,&nbsp;Lalita Bhanu Murthy ,&nbsp;Aneesh Krishna ,&nbsp;Sanjay Misra","doi":"10.1016/j.cola.2025.101322","DOIUrl":"10.1016/j.cola.2025.101322","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Context:&lt;/h3&gt;&lt;div&gt;The quality and design of Service-Based Systems may be degraded because of frequent changes, and negatively impacts the software design quality called &lt;strong&gt;Anti-patterns&lt;/strong&gt;. The existence of these Anti-patterns highly impacts the overall maintainability of Service-Based Systems. Hence, early detection of these anti-patterns’ presence becomes mandatory with co-located modifications. However, it is not easy to find these anti-patterns manually.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Objective:&lt;/h3&gt;&lt;div&gt;The objective of this work is to explore the role of WSDL (Web Services Description Language) metrics (MLAPW) for anti-pattern prediction using a Machine Learning (ML) based framework. This framework encompasses different variants of feature selection techniques, data sampling techniques, and a wide range of ML algorithms. This work empirically investigates the predictive ability of anti-pattern prediction models developed using different sets of WSDL metrics. Our major focus is to investigate ’&lt;em&gt;how these metrics accurately predict different types of Anti-patterns present in the WSDL file&lt;/em&gt;’.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods:&lt;/h3&gt;&lt;div&gt;To achieve the objective, different sets of WSDL metrics such as Structural Quality Metrics, Procedural Quality Metrics, Data Quality Metrics, Quality Metrics, and Complexity metrics, are used as input for Anti-patterns prediction models. Since these models use WSDL metrics as input, we have also used feature selection methods to find the best sets of WSDL metrics. These models are trained using various machine-learning techniques. This study also shows the performance of these models trained on balanced data using data sampling techniques. Finally, the empirical investigation of these techniques was done using accuracy and ROC (receiver operating characteristic curve) curve (AUC) with hypothesis testing.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results:&lt;/h3&gt;&lt;div&gt;The empirical study’s observation is based on 226 WSDL files from various domains such as finance, tourism, health, education, etc. The assessment asserts that the models trained using WSDL metrics have 0.79 mean AUC and 0.90 Median AUC. However, the models trained using the selected feature with classifier feature subset selection (CFS) have a better mean AUC of 0.80 and median AUC of 0.97. The experimental results also confirm that the models trained on up-sampling (UPSAM) have a better mean AUC of 0.79 and median AUC of 0.91 with a low value of Friedman rank of 2.40. Finally, the models trained using the least square support vector machine (LSSVM) achieved 1 median AUC, 0.99 mean AUC, and a low Friedman rank of 1.30.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion:&lt;/h3&gt;&lt;div&gt;The experimental results show that the AUC values of the models trained using Data and Procedural Quality Metrics are high as compared to the other sets of metrics. However, the models improved significantly in their prediction performance after employing feature selection techniques. The experimental result","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"83 ","pages":"Article 101322"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPotion: Embedding GPU programming in Elixir","authors":"André Rauber Du Bois,&nbsp;Gerson Geraldo H. Cavalheiro","doi":"10.1016/j.cola.2025.101323","DOIUrl":"10.1016/j.cola.2025.101323","url":null,"abstract":"<div><div>This paper describes GPotion, a DSL for GPU programming embedded in the Elixir functional language. GPotion allows programmers to write low-level GPU kernels, similar to CUDA kernels, in Elixir but also provides high-level facilities, like garbage collection of host and device arrays allocated in the host, type inference and simplified data transfer. This paper describes the design and implementation of GPotion and also presents experiments that demonstrate that GPotion allows fast and efficient kernels with little overhead in comparison to pure CUDA. GPotion is implemented using metaprogramming features of Elixir, without having to modify Elixir’s compiler. The source code for GPotion and the benchmarks used in the experiments are available in a GitHub repository.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"83 ","pages":"Article 101323"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The evolution of Lua, continued","authors":"Roberto Ierusalimschy ,&nbsp;Luiz Henrique de Figueiredo ,&nbsp;Waldemar Celes","doi":"10.1016/j.cola.2025.101326","DOIUrl":"10.1016/j.cola.2025.101326","url":null,"abstract":"<div><div>Lua is a scripting language created in 1993 in Brazil. We have reported in detail on the birth of Lua and its evolution until 2007. Here, we chronicle the evolution of Lua since then. In particular, we discuss in detail the evolution of global variables, the introduction of integers, and the implementation of garbage collection and finalizers, including deterministic finalization. We also comment on some landmark social developments in the history of Lua.</div></div>","PeriodicalId":48552,"journal":{"name":"Journal of Computer Languages","volume":"83 ","pages":"Article 101326"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}