Language Aware Sorting: Empirical and ML Driven Performance Evaluation of Insertion Sort

Abstract— Aim/Background This study aims to systematically evaluate the practical runtime performance of the Insertion Sort algorithm across six widely used programming languages: C, C++, Go, Java, PHP, and Python. While Insertion Sort’s theoretical time complexity is well established, real-world execution performance may differ significantly due to variations in language paradigms, compilation strategies, and runtime environments. The research focuses on identifying how language-specific characteristics influence actual execution time. Methods The Insertion Sort algorithm was implemented uniformly in all six languages using a shared pseudocode to ensure consistency. Experiments were conducted under controlled conditions using input sizes of 1K, 10K, and 100K elements. Each dataset was tested with four different data arrangements: ascending, descending, nearly sorted, and random. Runtime measurements were collected and compared. Additionally, a Relative Execution Time Ratio (RETR) metric was introduced to provide a normalized and quantitative comparison of performance across languages. Results The experimental findings show clear performance differences among the programming languages. Go demonstrated the fastest runtimes across most scenarios, benefiting from its efficient compiled execution model. Java performed second-best, leveraging just-in-time (JIT) compilation and runtime optimizations. C and C++ showed moderate performance, influenced by compiler optimization levels and memory management mechanisms. PHP and Python exhibited significantly slower execution times, particularly for large and poorly ordered datasets, mainly due to interpreter overhead and runtime inefficiencies. The RETR metric effectively highlighted these relative performance gaps. Conclusion The study confirms that programming language choice significantly impacts real-world algorithm performance, even for well-understood algorithms like Insertion Sort. Compiled languages with efficient runtime optimizations generally outperform interpreted languages, especially for larger datasets. These results emphasize the importance of selecting appropriate programming languages for performance-critical applications and aligning algorithm implementation with both data characteristics and execution environments.

One of the basic problem in computer science is to arrange the items in lexicographic order. Sorting is one of the major research topic. There are number of sorting algorithms. This paper presents the implementation and detailed analysis of library sort. Library sort is also called gapped insertion sort. It is a sorting algorithm that uses insertion sort with gaps. Time taken by insertion sort is O (n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) because each insertion takes O (n) time; and library sort has insertion time O (log n) with high probability. Total running time of library sort is O (n log n) time with high probability. Library sort has better run time than insertion sort, but the library sort also has some issues. The first issue is the value of gap which is denoted by `ε', the range of gap is given, but it is yet to be implemented to check that given range is satisfying the concept of library sort algorithm. The second issue is re-balancing which accounts the cost and time of library sort algorithm. The third issue is that, only a theoretical concept of library sort is given, but the concept is not implemented. So, to overcome these issues of library sort, in this paper, we have implemented the concept of library sort and done the detailed experimental analysis of library sort algorithm, and measure the performance of library sort algorithm on a dataset.

Three key forces are shaping the modern Computer Science (CS) curriculum: (1) new topics/courses are squeezing out existing ones; (2) a focus on "big picture" and interdisciplinary aspects of CS is leading to curricula in which the traditional core courses + electives model is being superseded by a more flexible approach based on tracks/threads; and (3) project-based courses are increasingly relying on a notion of just-in-time teaching in which particular skills are not bundled into a particular course, but are covered at a point when they are needed for particular project work. The undergraduate programming language curriculum is feeling the pressure of these forces. Core courses on programming languages and compilers are being changed to electives, relegated to a software/systems track, or phased out altogether. Particular programming languages and programming language concepts are being taught in a more piecemeal fashion on an as-needed basis. Unfortunately, these changes make it increasingly likely that CS majors can graduate without being exposed to certain "big ideas" of programming languages. Programming languages (along with many other subdisciplines of CS) contains ideas that every well-educated computer scientist needs to know and which are relevant to a wide variety of projects/careers. In a CS curriculum based on tracks and projects, how can we ensure that vital intellectual components are not lost in the shuffle? One way to think about this problem is to view the CS curriculum as a collection of aspects that can be combined in different arrangements to produce various projects/courses. In this paper, we consider the benefits and challenges of an aspect-oriented view of the CS curriculum, particularly in regard to programming language concepts.

Landslide is one of the most destructive natural hazards threatening the life of people and their properties. Mountainous areas are at a high risk of landslide occurrence. One-third of Iran is mountainous areas where landslide susceptibility modeling is required for sustainable land development. In this research for landslide susceptibility modeling, several supervised machine learning models, including Random Forest, eXtreme Gradient Boosting, the Salp Swarm Algorithm Multi-layer Perceptron (SSAMLP), and the Multi-verse Optimization Multi-layer Perceptron (MVOMLP), are developed in R and MATLAB programming languages. The Random Forest and eXtreme Gradient Boosting models are optimized based on their hyper-parameters in the R programming language using the caret library. In this research, landslide susceptibility models divide the study area into very low (class 1), low (class 2), moderate (class 3), high (class 4), and very high (class 5) landslide susceptibility zones. Results show that the eXtreme Gradient Boosting model has the best performance with values of 0.00225 and 0.00805 for the Mean Absolute Error and Root Mean Squared Error indices, respectively, for the training dataset. For the test dataset, the eXtreme Gradient Boosting model with the values of 0.0599 for the Mean Absolute Error, respectively, outperformed the Random Forest and the MVOMLP and the SSAMLP models. In terms of Mean Absolute Error, the SSAMLP algorithm has the best performance over the other three supervised models with a value of 0.1472.

Research on sorting algorithms is a very core research field in computer science. Today, any advancement in sorting algorithms would lead to less time and space required to perform the sorting task. This paper discusses a newly proposed algorithm—‘ledger-based sorting algorithm’. It is basically an advancement to ‘counting sort’, but not exactly counting sort. The purpose of this paper is to reduce the time and space required for sorting when the numbers given to you belong to a particular range (0–n) and all of them are distinct. This paper also compares the newly proposed algorithm with insertion sort, bubble sort, selection sort, quick sort, merge sort and mainly counting sort through graphical time analysis of these algorithms. The paper especially compares counting sort algorithm with the proposed algorithm in terms of both time and space complexity.

Sorting is the procedure of arranging the elements in ascending or descending order. Sorting algorithms are not only used in computer science but also in our day-to-day life to reduce complexity. For optimizing the use of other algorithms, efficient sorting is required. This paper makes a comparison between merge sort, quick sort, selection sort and insertion sort by their time complexities.

In this chapter it will be shown that there can exist possible connections of Tsallis nonextensive definition of entropy (Tsallis, 1988) with the statistical analysis of simple insertion sort algorithm behaviour. This will be done basing on the connections between the idea of Turing machines (Turing, 1936) as a basis of considerations in computer science and especially in algorithmic processing and the proposal of non-equilibrium thermodynamics given by Constatino Tsallis (Tsallis, 1988; Tsallis, 2004) for indication of the possible existence of non-equilibrium states in the case of one sorting algorithm behaviour. Moreover, it will be also underlined that a some kind of paradigm change (Kuhn, 1962) is needed in the case of computer systems analysis because if one considers the computers as physical implementations of Turing machines should take into account that such implementations always need energy for their work (Strzalka, 2010) – Turing machine as a mathematical model of processing does not need energy. Because there is no (computer) machine that have the efficiency η = 100%, thus the problem of entropy production appears during their work. If we note that the process of sorting is also the introduction of order (obviously, according to a given appropriate relation) into the processed set (sometimes sorting is considered as an ordering (Knuth, 1997)), thus if one orders it must decrease the entropy in sorted set and increase it somewhere else (outside the Turing machine – in physical world outside its implementation). The connections mentioned above will be given basing on the analysis of insertion sorting, which behaviour for some cases can lead to the levels of entropy production that can be considered in terms of non-extensivity. The presented deliberations can be also related to the try of finding a new thermodymical basis for important part of authors' interest, i.e., the physics of computer processing.

The rapid growth of the Internet of Things (IoT) and its applications requires high computational efficiency, low-cost, and low-power solutions for various IoT devices. These include a wide range of microcontrollers that are used to collect, process, and transmit IoT data. ESP32 is a microcontroller with built-in wireless connectivity, suitable for various IoT applications. The ESP32 chip is gaining more popularity, both in academia and in the developer community, supported by a number of software libraries and programming languages. While low- and middle-level languages, such as C/C++ and Rust, are believed to be the most efficient, TinyGo and MicroPython are more developer-friendly low-complexity languages, suitable for beginners and allowing more rapid coding. This paper evaluates the efficiency of the available ESP32 programming languages, namely C/C++, MicroPython, Rust, and TinyGo, by comparing their execution performance. Several popular data and signal processing algorithms were implemented in these languages, and their execution times were compared: Fast Fourier Transform (FFT), Cyclic Redundancy Check (CRC), Secure Hash Algorithm (SHA), Infinite Impulse Response (IIR), and Finite Impulse Response (FIR) filters. The results show that the C/C++ implementations were fastest in most cases, closely followed by TinyGo and Rust, while MicroPython programs were many times slower than implementations in other programming languages. Therefore, the C/C++, TinyGo, and Rust languages are more suitable when execution and response time are the key factors, while Python can be used for less strict system requirements, enabling a faster and less complicated development process.

The Fourth Industrial Revolution (4IR) has significantly impacted healthcare, including sexually transmitted infection (STI) management in Sub-Saharan Africa (SSA), particularly among key populations (KPs) with limited access to health services. This review investigates 4IR technologies, including artificial intelligence (AI) and machine learning (ML), that assist in diagnosing, treating, and managing STIs across SSA. By leveraging affordable and accessible solutions, 4IR tools support KPs who are disproportionately affected by STIs. Following systematic review guidelines using Covidence, this study examined 20 relevant studies conducted across 20 SSA countries, with Ethiopia, South Africa, and Zimbabwe emerging as the most researched nations. All the studies reviewed used secondary data and favored supervised ML models, with random forest and XGBoost frequently demonstrating high performance. These tools assist in tracking access to services, predicting risks of STI/HIV, and developing models for community HIV clusters. While AI has enhanced the accuracy of diagnostics and the efficiency of management, several challenges persist, including ethical concerns, issues with data quality, and a lack of expertise in implementation. There are few real-world applications or pilot projects in SSA. Notably, most of the studies primarily focus on the development, validation, or technical evaluation of the ML methods rather than their practical application or implementation. As a result, the actual impact of these approaches on the point of care remains unclear. This review highlights the effectiveness of various AI and ML methods in managing HIV and STIs through detection, diagnosis, treatment, and monitoring. The study strengthens knowledge on the practical application of 4IR technologies in diagnosing, treating, and managing STIs across SSA. Understanding this has potential to improve sexual health outcomes, address gaps in STI diagnosis, and surpass the limitations of traditional syndromic management approaches.

Today, among other challenges, teaching students how to write computer programs for the first time can be an important criterion for whether students in computing will remain in their program of study, i.e. Computer Science or Information Technology. Not learning to program a computer as a computer scientist or information technologist can be compared to a mathematician not learning algebra. For a mathematician this would be an extremely limiting situation. For a computer scientist, not learning to program imposes a similar severe limitation on the budding computer scientist. Therefore it is not a question as to whether programming should be taught rather it is a question of how to maximize aspects of teaching programming so that students are less likely to be discouraged when learning to program. Different criteria have been used to select first programming languages. Computer scientists have attempted to establish criteria for selecting the first programming language to teach a student. This paper examines the criteria used to select first programming languages and the issues that novices face when learning to program in an effort to create a more comprehensive model for selecting first programming languages.

Computer science (CS) is defined in wikipedia as a branch of human knowledge “relating to computation, ranging from abstract analysis of algorithms and formal grammars, to subjects like programming languages, software, and computer hardware” (Computer Science, 2005). Computer science emerged as a distinct field in the 1940s and 1950s with the development of the first electronic digital computers. To limit computer science to just computer use or its knowledge bodies, however, is reductive; CS is embedded in a complex, unquantifiable cultural context, including socio-economic and gendering practice. Computer hardware and software are designed to complement and supplement human activity and processes such as warfare, industrial applications, information management, including education, the Internet, a knowledge commons, and most recently biotechnology. Although CS is typically considered neutral and scientific, its episteme and practice is androcentric or male centered, often to the exclusion of females (Herbst, 2002). Female attributes have not typically been associated with computer science or computers. Although there is general agreement that women are as intellectually capable as men in CS, the fact remains that women today do not have equal participation in CS majors, CS engineering, programming, software design, Web site construction, or computer repair. (Jepson &amp; Perl, 2002). In the technetronic 21st century, when computers are becoming standard for education and in symbolic analytic jobs, women’s enrollment in CS has declined, and many women do not feel confident using computers for more than e-mail transmissions, e-commerce, and social interaction in forums or newsgroups. Women who do not have knowledge or confidence in their abilities to work in CS not only have unrealized potentials in CS but also are left out of employment activities. Reasons for gendering in CS are complex and debated. Socialization, overt and tacit discrimination, and epistemological plurality are three dominant explanations. CS industries, educators, cognitive scientists, parents, and women professionals in CS are some of the groups currently working to attain gender equity in CS.

Data partitioning algorithms aiming to minimize the execution time and the energy of computations in self-adaptable data-parallel applications on modern extreme-scale multicore platforms must address two critical challenges. First, they must take into account the new complexities inherent in these platforms such as severe resource contention and non-uniform memory access. Second, they must have low practical runtime and memory costs. The sequential data partitioning algorithms addressing the first challenge have a theoretical time complexity of O( $m * m * p * p$ ) where $m$ is the number of points in the discrete speed/energy function and $p$ is the number of available processors. They, however, exhibit high practical runtime cost and excessive memory footprint, therefore, rendering them impracticable for employment in self-adaptable applications executing on extreme-scale multicore platforms. We present, in this paper, the parallel data partitioning algorithms that address both the challenges. They take as input the functional models of performance and energy consumption against problem size and output workload distributions, which are globally optimal solutions. They have a low time complexity of O( $m * m * p$ ) thereby providing a linear speedup of O( $p$ ) and low memory complexity of O( $n$ ) where $n$ is the workload size expressed as a multiple of granularity. They employ dynamic programming approach, which also facilitates the easier integration of performance and energy models of communications. We experimentally study the practical cost of application of our algorithms in two data-parallel applications, matrix multiplication and fast Fourier transform, on a cluster in Grid’5000 platform. We demonstrate that their practical runtime and memory costs are low making them ideal for employment in self-adaptable applications. We also show that the parallel algorithms exhibit tremendous speedups over the sequential algorithms. Finally, using theoretical analysis for a forecast exascale platform, we demonstrate that the parallel algorithms have negligible execution times compared to the matrix multiplication application executing on the platform.

Computer science is a higher education domain that still show a significant male dominance. Many research studies have highlighted the importance of diversity and gender balance in computer science related areas such as software engineering and system development. However, there is still a well-identified problem that university programmes and courses on computer science fail to attract the female audience. The objective of this study is to investigate the concept of gender-inclusive computer science (CS) education with the aim of broadening participation in CS courses and programs. This is conducted through a literature study, initially focusing on keywords and research areas, and subsequently searching into existing research. The research question that guided the study was: "What concepts can be found in literature to make computer science education more gender-inclusive?". Data were analysed thematically in a two-step analysis process inspired by the grounded theory methods of Open coding and Axial coding. Findings suggest that there is significant room for learning in this field, particularly from Critical CS education studies. The Open coding analysis showed that the findings can be categorised into eight main themes. In the Axial coding the themes were merged, refined, renamed, and centred around the main axial theme of 'Epistemological pluralism'. Other essential themes that all are related to the axial main theme were: 'Design and creativity', 'Bias awareness and ethics', 'Collaboration and communication', 'Self-regulated learning', 'Real-world applications', and 'Role models and mentorship'. The result of this study is presented through a visual model that illustrates essential aspects of inclusive computer science education. The paper also proposes various directions for future research.

In this paper, we investigate the influence of various factors, such as programming language, testing environment, and input data, on the accuracy of algorithm execution measurements. To conduct this study, we used the BubbleSort algorithm as a test case and implemented it in Java, C, and x86 assembly language. We executed these implementations with various inputs and performed an empirical evaluation of the results using the ALGator system. We showed that the influence of the chosen programming language is negligible, since the Java and C implementations gave very similar results, while the assembler implementation differed only by a constant factor. Furthermore, our analysis emphasized the importance of repeating tests to obtain precise timing measurements - the more tests we do, the more accurate the measured result will be. We also discuss the impact of the input data type which can significantly affect the execution time due to the increased number of mispredictions of the branch predictor.

The pipeline of three introductory computer science courses at our university have low pass rates. As part of our efforts to improve the pass rates in these courses, we examine the students' mathematics preparation and their progress from one course to the next. We find that the minority of students who enter the university with good mathematics preparation are likely to succeed in our introductory computer science courses. For the majority of students, we suggest a series of interventions throughout the pipeline. Prior to taking Computer Science 1, students could gain more experience with problem solving during the Introduction to Computer Science and Introduction to Computer Engineering courses. We suggest adding precalculus as a prerequisite for Computer Science 2. For students who do not earn an A in Computer Science 2, we plan to examine whether taking a class in a second programming language before taking Computer Science 3 will improve performance in Computer Science 3. Our goal is to improve student learning and retention through these efforts, combined with continual innovation in the classroom.