Program Comprehension Research Articles

Semantics Lifting for Syntactic Sugar

Syntactic sugar plays a crucial role in engineering programming languages. It offers convenient syntax and higher-level of abstractions, as witnessed by its pervasive use in both general-purpose and domain-specific contexts. Unfortunately, the traditional approach of translating programs containing syntactic sugars into the host language can lead to abstraction leakage, breaking the promise of convenience and hindering program comprehension. To address this challenge, we introduce the idea of semantics lifting that aims to statically derive self-contained evaluation rules for syntactic sugars. More specifically, we propose a semantics-lifting framework that consists of (i) a general algorithm for deriving host-independent semantics of syntactic sugars from the semantics of the host language and the desugaring rules, (ii) a formulation of the correctness and abstraction properties for a lifted semantics, and (iii) a systematic investigation of sufficient conditions that ensure a lifted semantics is provably correct and abstract. To evaluate our semantics-lifting framework, we have implemented a system named Osazone and conducted several case studies, demonstrating that our approach is flexible, effective, and practical for implementing domain-specific languages.

An eye tracking study assessing source code readability rules for program comprehension

An eye tracking study assessing source code readability rules for program comprehension

Using technology to reduce learning costs and improve program comprehension: Lessons from a survey experiment on Supplemental Nutrition Assistance Program

AbstractThe Supplemental Nutrition Assistance Program (SNAP) provides food assistance to those in need, and while the program reaches many who are eligible, program participation falls short of reaching all who are eligible. One factor contributing to this gap in participation is difficulty understanding program eligibility, a common challenge with means‐tested benefit programs. Governments have attempted to improve public understanding of these complex programs using a range of tools to reduce learning costs, yet we know little about the extent to which these tools work and why. This preregistered survey experiment tests three commonly used methods of communicating SNAP program information, including a flyer, screening tool, and video. Cumulatively, our findings demonstrate that efforts to reduce SNAP learning costs are generally effective at improving comprehension recall and that the type of intervention matters, with the video increasing comprehension scores more than the flyer and screening tool. However, the impact of the intervention does not vary substantially according to household income.

Assessment of Fire Safety Measures in On-Campus Housing Facilities

Universities, including Caleb University, offer various on-campus housing options, such as hostels and private residences. Ensuring fire safety in these facilities is paramount for the protection of students, staff, and property. This study investigates the fire safety measures in place at Caleb University's male hostel, examining both the physical infrastructure and the awareness and training of residents and staff. Using a mixed-methods approach, the study combines a physical inspection of fire safety equipment and infrastructure with semi-structured interviews to assess the comprehensiveness of fire safety protocols and awareness programs. The findings reveal significant gaps in both the awareness and implementation of fire safety measures. Only 33% of the staff and 69% of students are aware of any fire safety programs, and 86% of the staff and 65% of the students lack knowledge of evacuation protocols. These deficiencies highlight the need for structured and mandatory fire safety training, regular drills, and clear communication of evacuation procedures. Recommendations include the development of comprehensive fire safety programs, integration of fire safety education into the curriculum, regular maintenance of fire safety equipment, and partnerships with local fire safety authorities. Implementing these measures can improve the safety and preparedness of Caleb University’s residential facilities, providing a safer living environment for all residents.

Exploring genotype by environment interaction in sunflower using genotype plus genotype by environment interaction (GGE) and best linear unbiased prediction (BLUP) approaches

Sunflower is important for food, feed and ornamentals, faces rising demand for quality oil. Identifying resilient high-yielding genotypes is essential. Multi-environmental trials and integrated approaches to evaluate genotype by environment interactions (GEI) enhance accuracy and comprehensiveness of breeding programs. This study assess the representativeness and discriminative power of tested environments; classify mega-environments for genotype recommendations and evaluate how GGE and BLUP methods identify stable and superior sunflower genotypes. The experiment used a randomized complete block design with three replications across six locations for two seasons. The pooled analysis of variance for GEI on seed yield showed significant variability (p < 0.05) with mean values from 1171 to 2564 kg ha−1, indicating inconsistent genotypes performance across environments. The study identified E11, E2, and E6 as key discriminating and representative environments and grouped test locations into four mega-environments. Genotypes G6, G1, and G4 are the most desirable based on stability and seed yield. E11 and G6 are identified as ideal environment and genotype, respectively. GGE biplot and BLUP methods consistently identify stable and ideal sunflower genotypes. However, GGE stability indices differ somewhat in assessing genotypes stability possibly due to low number of genotypes, testing environment, and year in the dataset. The study confirmed that simultaneous consideration of GGE and BLUP models for GEI analysis helps to select more stable and adaptable sunflower genotypes across environments. It also clarifies GEI pattern, identifying mega-environments, and aiding genotype selection in sunflower production. However, validating these mega-environments and exploring the genetic basis of adaptability and stability remains essential.

A Method to Quantitative Compare Obfuscating Ttransformations

The paper considers the problem of quantitative comparison of potency and resistance of practically applied obfuscating transformations of program code. A method is proposed to find the potency and resistance of transformations by calculating the «comprehensibility» of the obfuscated and deobfuscated versions of a program, respectively. As a measure of program comprehensibility, it is proposed to use the similarity of this program to the approximation of its «most comprehensible» version. Based on the proposed method a model to assess potency and resistance was built, the main elements of which are: a set of investigated obfuscating transformations, a similarity function, a method to approximate the most comprehensible version of the program and a deobfuscator. To implement this model 1) obfuscating transformations provided by Hikari obfuscator are chosen; 2) 8 similarity functions are constructed by machine learning methods using static characteristics of programs from CoreUtils, PolyBench and HashCat sets; 3) the smallest program version was chosen as an approximation of the most comprehensible program version (found among the versions obtained using optimization options of GCC, Clang and AOCC compilers); 4) a program deobfuscation scheme based on the optimizing compiler from LLVM was built and implemented. The results of the potency and resistance for sequences of transformations of lengths one, two and three were experimentally obtained. These results showed consistency with the results of independent potency and resistance evaluations obtained by other methods. In particular, it was found that the highest potency and resistance are demonstrated by sequences of transformations starting with transformations of the control flow graph, and the lowest resistance and potency are generally demonstrated by sequences that do not contain such transformations.

Beyond code: Is there a difference between comments in visual and textual languages?

Code comments are crucial for program comprehension and maintenance. To better understand the nature and content of comments, previous work proposed taxonomies of comment information for textual languages, notably classical programming languages. However, paradigms such as model-driven or model-based engineering often promote the use of visual languages, to which existing taxonomies are not directly applicable. Taking MATLAB/Simulink as a representative of a sophisticated and widely used modeling environment, we extend a multi-language comment taxonomy onto new (visual) comment types and two new languages: Simulink and MATLAB. Furthermore, we outline Simulink commenting practices and compare them to textual languages. We analyze 259,267 comments from 9095 Simulink models and 17,792 MATLAB scripts. We identify the comment types, their usage frequency, classify comment information, and analyze their correlations with model metrics. We manually analyze 757 comments to extend the taxonomy. We also analyze commenting guidelines and developer adherence to them. Our extended taxonomy, SCoT (Simulink Comment Taxonomy), contains 25 categories. We find that Simulink comments, although often duplicated, are used at all model hierarchy levels. Of all comment types, Annotations are used most often; Notes scarcely. Our results indicate that Simulink developers, instead of extending comments, add new ones, and rarely follow commenting guidelines. Overall, we find Simulink comment information comparable to textual languages, which highlights commenting practice similarity across languages.

The Framework of Construction Workers Safety: Evaluating the Effectiveness of Safety Practices in Construction Industry

Abstract: This study aims to investigate the multifaceted approach to safety and health management practices within the construction industry, with the ultimate goal of delineating their impact on reducing workplace incidents and enhancing overall safety culture. In order to achieve this goal, a mixed-methods research design is employed, which combines quantitative data from structured surveys across a diverse sample of construction sites with qualitative insights from in-depth interviews and case studies. The findings of the analysis reveal a significant correlation between the frequency and comprehensiveness of safety training programs and a reduction in incident rates. Furthermore, the adoption of advanced safety technologies and equipment, coupled with a strong organizational safety culture, is found to be a pivotal factor in improving safety outcomes. The study also highlights the importance of customizing safety practices to the unique needs of different construction projects to optimize effectiveness.

Modeling source code in bimodal for program comprehension

Modeling source code in bimodal for program comprehension

EL Greco Platform: A novel Python programming learning platform that uses a real robot

AbstractThis paper introduces the El Greco Platform, a Python programming platform for distance learning that employs an educational robot. This website allows prospective learners to remotely control Greco, a social humanoid robot designed to be cost‐effective, simple to construct, and appropriate for use in education. El Greco is capable of performing multiple tasks, including combined movements. These Robot capabilities can be programmed using either Python code or the Blockly library, which adds an editor to an application that visualizes coding concepts as interlocking blocks. Programming a robot appears to be a significantly more effective and creative method for students to learn a programming language. This educational tool was designed primarily for use by students and allows anyone to learn Python while controlling a robot for free. El Greco Platform features gamification elements that increase the enjoyment and engagement of the learning experience while reinforcing the concepts taught. The survey results on students aged 13–18 revealed that the El Greco Platform captivated the study participants and positively affected their attitudes toward programming and robotics. In addition, it significantly impacted their comprehension of programming and motivated them to seek additional opportunities to expand their knowledge of robotics and programming.

Multi-Intent Inline Code Comment Generation via Large Language Model

Code comment generation typically refers to the process of generating concise natural language descriptions for a piece of code, which facilitates program comprehension activities. Inline code comments, as a part of code comments, are also crucial for program comprehension. Recently, the emergence of large language models (LLMs) has significantly boosted the performance of natural language processing tasks. This naturally inspires us to explore the performance of the LLMs in the task of inline code comment generation. To this end, we evaluate open-source LLMs on a large-scale dataset and compare the results with the current state-of-the-art methods. Specifically, we explore the model performance in the following scenarios based on the widely used evaluation metrics (i.e. BLEU, Meteor, and ROUGE-L): (1) generation with simple instruction; (2) few-shot-guided generation with random examples selected from the database; (3) few-shot-guided generation with similar examples selected from the database; and (4) adopt the re-ranking strategy for the output of LLMs. Our findings reveal that: (1) under the simple instruction scenario, LLMs could not fully show the potential in the task of inline comment generation compared to the state-of-the-art models; (2) random few-shot leads to a slight improvement; (3) similar few-shot and re-ranking strategy could significantly enhance the performance of LLMs; and (4) for inline comment and code snippet pairs with different intents, why category achieves the best performance and what category achieves relatively poorer performance. That remains consistent across all four scenarios. Our findings shed light on future research directions for using LLMs in inline comment generation tasks.

Assessment of the comprehensiveness of paediatric national immunisation programmes in Europe: expert validation and future perspectives

ABSTRACT Background The breadth of protection of National Immunisation Programmes (NIPs) across Europe varies, however, this has not been assessed within published literature. Therefore, a framework was developed to assess the comprehensiveness of pediatric NIPs in Europe. This study aimed to validate and further develop criteria used to cluster countries into three tiers. Research design and methods Independent Europe-based experts (n = 23) in the field of pediatric vaccination were invited to participate in a double-blinded modified Delphi panel, with two online survey rounds and a virtual consensus meeting. Consensus was defined as ≥ 80% of experts rating their agreement/disagreement on a 9-point Likert scale. Results The number of preventable diseases covered by an NIP, simplification of the vaccination calendar, strengthened protection by increasing serotype, degree of funding and epidemiological factors were considered key concepts for consideration of the comprehensiveness of pediatric NIPs in Europe. Experts highlighted that the framework should be extended to include adolescent vaccines and populations up to 18 years of age. Consensus regarding further amendments to the framework was also reached. Conclusions This Delphi panel validated a framework to assess the comprehensiveness of European NIPs. The framework can be used to facilitate discussions to help countries improve and expand the breadth of protection provided by their NIP.

Statement-Grained Hierarchy Enhanced Code Summarization

Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features, causing limited code representation. Addressing this gap, our paper introduces the statement-grained hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations.

Comprehension of Financial Literacy Programs in India as Gateway to Empowering Women: Gauging through Case Study Approach

Overtime India has emerged as a global economic powerhouse; certain demographic groupings still lack general knowledge about economics and money. There has been an increasing recognition of the women entrepreneurs playing a substantial role in driving the economic growth and fostering innovation. Governments around the world have progressively focused their attention on supporting and empowering women in the business landscape. In India, various government schemes and initiatives have been introduced to provide women entrepreneurs with the necessary tools, resources, and opportunities to succeed in the competitive business world. Existing financial literacy programs for Indian female entrepreneurs need evaluation to gauge effectiveness, measure their impact, and identify participation barriers. This research aims to assess programs, study their influence, and inform improved initiatives. The study attempts in showcasing real-life success stories of the women entrepreneurs who have benefited from the scheme's financial assistance and mentorship, potential challenges, and its contribution to fostering a culture of the women entrepreneurship &amp; the social entrepreneurship. The study assesses the effectiveness of these programs in providing guidance, skill enhancement, and networking opportunities to the aspiring and also the established women entrepreneurs. While taking into account the many socioeconomic environments that women operate in, the study offers insights into the policy measures that can best promote and empower women to become active participants in economic growth.

Impacts of authentic texts on EFL students' reading comprehension

The purpose of this research was to explore the effect of authentic texts on students' reading comprehension of the English Education Study Program at the University of Bengkulu. This research applied a quantitative method, in the form of a quasi-experimental study. The subjects of the research were 68 sixth-semester students in which 34 students as the experimental group and 34 other students as the controlled group. The experimental group got treatment of authentic text, while the controlled group got treatment of textbooks. The instrument of the research used was the test. The mean score of the experimental group was higher than the scores of the controlled group (75.88: 68.24). The t-test calculation = (p&lt;α); (0.005&lt;0.05). It means alternative hypothesis was accepted, and null hypothesis was rejected. In other words, there was an effect of using authentic texts on students’ reading comprehension. It was recommended that it was a good idea for English lecturers, apart from other texts, pay close attention to authentic texts as well.

A Model of How Students Engineer Test Cases With Feedback

Background and Context. Students’ programming projects are often assessed on the basis of their tests as well as their implementations, most commonly using test adequacy criteria like branch coverage, or, in some cases, mutation analysis. As a result, students are implicitly encouraged to use these tools during their development process (i.e., so they have awareness of the strength of their own test suites). Objectives. Little is known about how students choose test cases for their software while being guided by these feedback mechanisms. We aim to explore the interaction between students and commonly used testing feedback mechanisms (in this case, branch coverage and mutation-based feedback). Method. We use grounded theory to explore this interaction. We conducted 12 think-aloud interviews with students as they were asked to complete a series of software testing tasks, each of which involved a different feedback mechanism. Interviews were recorded and transcripts were analyzed, and we present the overarching themes that emerged from our analysis. Findings. Our findings are organized into a process model describing how students completed software testing tasks while being guided by a test adequacy criterion. Program comprehension strategies were commonly employed to reason about feedback and devise test cases. Mutation-based feedback tended to be cognitively overwhelming for students, and they resorted to weaker heuristics in order to address this feedback. Implications. In the presence of testing feedback, students did not appear to consider problem coverage as a testing goal so much as program coverage . While test adequacy criteria can be useful for assessment of software tests, we must consider whether they represent good goals for testing, and if our current methods of practice and assessment are encouraging poor testing habits.

Decoding Success: Assessing the Online Pseudocode Interpreter through Users Insights

Pseudocode is vital in enhancing programming comprehension, making it an essential component of the Programming Logic Formulation (CS1) course. However, many CS1 students from various senior high school strands struggle with this concept, highlighting the need for additional learning resources. This research evaluated a pseudocode interpreter designed for first-year Information and Communications Technology (ICT) students at Iloilo Science and Technology University in Iloilo City, Philippines. Using random sampling, 147 first-year ICT students were selected to evaluate the software's user experience in usability, credibility, appearance, and loyalty using the Standardized User Experience Percentile Rank Questionnaire (SUPR-Q). Likewise, five ICT experts were selected using purposive sampling to evaluate the system's validity as instructional material based on its content and on the material's organization, form, and style using the university's Quality Form for Instructional Materials (IM). The user experience evaluation yielded positive results, with students unanimously agreeing on the software's ease of use, dependability, and user-friendliness. The expert evaluation confirmed the system's validity as instructional material, commending its content, organization, form, and style. These findings suggest that the pseudocode interpreter provides immediate benefits for students, offering a valuable learning tool. Moreover, the software has the potential to be adopted by other faculty members, promoting a more efficient and captivating learning experience. Future research could investigate how interpreters affect student learning and their usefulness in various educational settings.

Gender Mainstreaming in Climate Policy: Empowering Women and Reducing Gender Disparities in Post-Disaster Scenarios

Gender mainstreaming involves integrating a gender perspective into various actions, policies, legislations, or initiatives to address the concerns of all individuals and prevent the perpetuation of gender inequalities through institutional channels. Despite its global implementation, gender mainstreaming has not consistently resulted in progress for women; often, it is linked to a scaling back of policies and programs specifically targeting women. Recent research highlights the gendered impacts of climate change, yet efforts to develop mitigation and adaptation strategies have largely neglected gender mainstreaming. Additionally, the predominant scientific and technological focus in institutional responses has overshadowed social outcomes, leading to insufficient attention to vulnerable groups, particularly women. This paper argues for the imperative of gender mainstreaming in climate policy and advocates for policies explicitly centered on women's empowerment. The essence of gender mainstreaming is crucial not only for ensuring the comprehensiveness of climate policies and programs but also for the development of women-focused policies, ensuring that women receive the necessary support and empowerment to take proactive measures on their own behalf.

TRANSPARENCY AND EFFECTIVENESS IN MANAGING THE DIRECT CASH ASSISTANCE FOR VILLAGE FUNDS (BLTDD) IN THE VILLAGE OF BUNGINGKELA, SUBDISTRICT OF BUNGKU SELATAN, MOROWALI REGENCY

This study aims to investigate and analyze the transparency and effectiveness of the Direct Cash Assistance for Village Funds (BLTDD). The variables in this research consist of transparency and effectiveness. Data collection techniques in this study involve interviews and documentation. The informants in this research include the village head, village secretary, village treasurer, village consultative board (BPD) chairperson, and BLTDD recipients. The data analysis used in this study is a qualitative descriptive analysis. The results of this research indicate that transparency in the management of BLTDD in Bungingkela Village, Bungku Selatan Subdistrict, and Morowali Regency has not fully adhered to transparency principles. This is evidenced by the indicators of document availability and accessibility, as the Bungingkela Village government has not disseminated the list of potential BLTDD recipients. The effectiveness of BLTDD management in Bungingkela Village, Bungku Selatan Subdistrict, and Morowali Regency, assessed based on program comprehension, targeting accuracy, timeliness, goal achievement, and tangible changes, can be concluded to have been effectively implemented.

Assessing the Effect of Programming Language and Task Type On Eye Movements of Computer Science Students

Background and Context: Understanding how a student programmer solves different task types in different programming languages is essential to understanding how we can further improve teaching tools to support students to be industry-ready when they graduate. It also provides insight into students’ thought processes in different task types and languages. Few (if any) studies investigate whether any differences exist between the reading and navigation behavior while completing different types of tasks in different programming languages. Objectives: We investigate whether the use of a certain programming language (C++ vs. Python) and type of task (new feature vs. bug fixing) has an impact on performance and eye movement behavior in students exposed to both languages and task types. Participants: Fourteen students were recruited from a Python course that taught Python as an introductory programming language. Study Method: An eye tracker was used to track how student programmers navigate and view source code in different programming languages for different types of tasks. The students worked in the Geany IDE (used also in their course) while eye tracking data was collected behind the scenes making their working environment realistic compared to prior studies. Each task type had a Python and C++ version, albeit on different problems to avoid learning effects. Standard eye tracking metrics of fixation count and fixation durations were calculated on various areas of the screen and on source code lines. Normalized versions of these metrics were used to compare across languages and tasks. Findings: We found that the participants had significantly longer average fixation duration and total fixation duration adjusted for source code length during bug fixing tasks than the feature addition tasks, indicating bug fixing is harder. Furthermore, participants looked at lines adjacent to the line containing the bug more often before looking at the buggy line itself. Participants who added a new feature correctly made their first edit earlier compared to those who failed to add the feature. Tasks in Python and C++ have similar overall fixation duration and counts when adjusted for character count. The participants spent more time fixating on the console output while doing Python tasks. Overall, task type has a bigger effect on the overall fixation duration and count compared to the programming language. Conclusions: CS educators can better support students in debugging their code if they know what they typically look at while bug fixing. For new feature tasks, training students not to fear edits to learn about the code could also be actively taught and encouraged in the classroom. CS education researchers can benefit by building better IDE plugins and tools based on eye movements that guide novices in recognizing bugs and aid in adding features. These results will lead to updating prior theories on mental models in program comprehension of how developers read and understand source code. They will eventually help in designing better programming languages and better methods of teaching programming based on evidence on how developers use them.