Warm Mix Asphalt (WMA) and Hot Mix Asphalt (HMA) are prepared at lower temperatures, making it more susceptible to moisture damage, which eventually leads to stripping due to the adhesion failure. Moreover, the assessment of the adhesion failure depends on the expertise of the investigator’s subjective visual assessment skills. Nowadays, image processing has gained popularity to address the inaccuracy of visual assessment. To attain high accuracy from image processing algorithms, the loss of pixels plays an essential role. In high‐quality image samples, processing takes more execution time due to the greater resolution of the image. Therefore, the execution time of the image processing algorithm is also an essential aspect of quality. This manuscript proposes a parallel k means for image processing (PKIP) algorithm using multiprocessing and distributed computing to assess the adhesion failure in WMA and HMA samples subjected to three different moisture sensitivity tests (dry, one, and three freeze‐thaw cycles) and fractured by indirect tensile test. For the proposed experiment, the number of clusters was chosen as ten (k = 10) based on k value and cost of k means function was computed to analyse the adhesion failure. The results showed that the PKIP algorithm decreases the execution time up to 30% to 46% if compared with the sequential k means algorithm when implemented using multiprocessing and distributed computing. In terms of results concerning adhesion failure, the WMA specimens subjected to a higher degree of moisture effect showed relatively lower adhesion failure compared to the Hot Mix Asphalt (HMA) samples when subjected to different levels of moisture sensitivity.

Is the given code the best? Or can it be improved? And if so, by how much? To answer these three questions, code cannot be seen in isolation from it’s developer as the developer factor plays an vital role in determining code quality. However, no universally accepted metric or developer stat currently exists that provides an objective indicator to a developer’s ability to produce code benchmarked against an expert developer. While traditional developer stats like rank, position, rating and experience published on Online Judges (OJs) provide various insights into a developer’s behavior and ability, they do not help us in answering the three questions. Moreover, unless code quality can be numerically quantified this may not be possible. Towards this end, we conducted an empirical study of over 72 million submissions made by 143,853 users in 1876 contests on Codeforces, a popular OJ, to analyze their code in terms of its correctness , completeness and performance efficiency (code quality characteristics listed in the ISO/IEC 25010 product quality model) measured against the given requirements regardless of the programming language. First, we investigated ways to predict code quality given a developer’s traditional stats using various ML regression models. To quantify and compare code quality, new concepts like score and contest scorecard , had to be introduced. Second, we identified causes that led to poor predictability. Our analysis helped classify user’s performance in contests based on our discovery of erratic or temperamental behavior of users during contests. Third, we formulated quality index of a developer or q-index , a new and unique developer stat to indicate the ability of a developer’s ability to produce quality code, to help improve the predictability of the ML models by mitigating the negative effect of temperamental behavior of users in contests. Among the ML models used, our results suggest that the GradientBoost regressor is the most suited ML model to predict code quality which gave us a high prediction accuracy of around 99.55%.We also demonstrated the uniqueness of q-index over traditional stats and described how it can be used in decision making.

This article explores a novel approach to evaluating developer performance and software quality by drawing parallels between programmers and football players. We highlight the inefficiencies in current assessment methods, which often rely on years of experience rather than quantitative metrics that truly reflect a developer's capabilities. By examining the historical evolution of both fields, we identify the significant advancements in performance analysis in football, which are rooted in statistical data. We propose a framework for developer analytics that utilizes performance metrics derived from programming contests, allowing for a more nuanced classification of programmers based on their skills and roles. Our findings suggest that adopting similar quantitative evaluation strategies in software development could enhance talent management, task allocation, and overall team productivity, ultimately driving progress in the industry.