The research examines the value and potential usefulness of using systemic thinking, which looks at the interconnectedness of things, to comprehend the complexities of software development projects and the technical and human factors involved. It considers two different aspects of systemic thinking - psychological and sociological - and posits that these can assist in understanding how software teams function and attain their objectives, as well as the goals of the entities for which they work. Our research aims to provide a novel contribution to the field by investigating the use of systemic thinking in software development teams and organizations. We evaluate the reliability and validity of the survey applied to different groups of relevant participants, relate our findings to existing literature, and identify the most representative factors of systemic thinking. Despite the popularity of various factors that fall under the umbrella of ’systems thinking’, there is limited understanding of their effectiveness in improving organizational performance or productivity, particularly when it comes to psychological and sociological systemic factors. The relationship between the use of systems thinking and organizational performance is often based on anecdotal evidence, rather than the identification and application of specific factors. Our work emphasizes the importance of understanding and applying such factors in order to build a solid foundation for the effective use of system dynamics and systems thinking tools, which is crucial for software development teams.

Automatically prioritizing software development tasks extracted from codes could provide significant technical and organizational advantages. Tools exist for the automatic extraction of tasks, but they still lack the ability to capture their mutual dependencies; hence, the capability to prioritize them. Solving this important puzzle is the goal of the presented industrial challenge.

Simple detection of bugs, defects or anomalies during software development is not enough - it is necessary to apply corrective actions to eliminate them. To find out whether an anomaly exists in any software, we can measure the quality attributes using software metrics. The main goal of this paper was to find out and explain how to meaningfully attribute metrics to useful corrective actions.

The paper is devoted to the problem of automatic geometry evaluation of the log moving through the conveyor.The video sequence obtained from the single camera is used as the input data.The principal restrictions of the target objects described for the given task, and the requirements to the video recording of the manufacturing process are formulated on the basis of datasets from more than .5Mvideo images.The authors' method for the video sequence segmentation in respect to the log tracking is presented.The algorithm is based on the combination of background subtraction techniques and probabilistic methods.Next part of the paper is devoted to the log geometry estimation methods.The authors' algorithm for the log geometry structure recovery is based on the detection, isolation and approximation of log boundaries.The results of the research are implemented in the development of the conveyor-tracking system for automatic log sorting.

The scrutiny of boundaries isolation methods is presented in this paper. The newly developed algorithms, based on regression analysis and integral projection are compared with Hough transform in order to analyze their effectiveness for the specific problem of moving logs control. The comparative analysis of the methods was carried out on the database of images obtained from video sequence of real industrial process by the criteria of accuracy and operation speed. Results of the test show that the line-by-line scanning method with posterior LOWESS regression analysis has the best accuracy. However, the best appropriate for the implementation in the real-time control systems based on machine vision technology is consecutive line selection method due to its reasonable accuracy and impressive performance.