Regression testing is primarily a maintenance activity that is performed frequently to ensure the validity of the modified software. In such cases, due to time and cost constraints, the entire test suite cannot be run. Thus, it becomes essential to select or prioritize the tests in order to cover maximum faults in minimum time. Recently, Ant Colony Optimization (ACO), which is a new way to solve time constraint prioritization problem, has been utilized. This paper presents the analysis of the regression test prioritization technique to reorder test suites in time constraint environment along with the sample runs on various programs. Our analysis concluded that the ACO finds better orderings at higher values of the time constraint (TC). The correctness of the technique has also been recorded to be near optimal at an average.

Software testing is undertaken to ensure that the software meets the expected requirements. The intention is to find bugs, errors, or defects in the developed software so that they can be fixed before deployment. Testing of the software is needed even after it is deployed. Regression testing is an inevitable part of software development, and must be accomplished in the maintenance phase of software development to ensure software reliability. The existing literature presents a large amount of relevant knowledge about the types of techniques and approaches used in regression test case selection and prioritization (TCS&P), comparisons of techniques used in TCS&P, and the data used. Numerous secondary studies (surveys or reviews) have been conducted in the area of TCS&P. This study aimed to provide a comprehensive examination of the analysis of the enhancements in TCS&P using a thorough systematic literature review (SLR) of the existing secondary studies. This SLR provides: (1) a collection of all the valuable secondary studies (and their qualitative analysis); (2) a thorough analysis of the publications and the trends of the secondary studies; (3) a classification of the various approaches used in the secondary studies; (4) insight into the specializations and range of years covered in the secondary texts; (5) a comprehensive list of statistical tests and tools used in the area; (6) insight into the quality of the secondary studies based on the seven selected Research Paper Quality parameters; (7) the common problems and challenges encountered by researchers; (8) common gaps and limitations of the studies; and (9) the probable prospects for research in the field of TCS&P.

Ant Colony Optimization (ACO) is a well known and rapidly evolving meta-heuristic technique. A large number of optimization problems have already taken advantage of the ACO technique while countless others are on their way. A copious amount of effort has also been put in by the researchers for applying ACO in solving various software testing problems. This paper presents a survey of twenty-one such studies, identified as relating to the use of ACO in diverse software testing concepts. To the best of our knowledge, no literature survey could be found published in the same context till date. Consequently, the twenty one studies have been rigorously analyzed to find some common parameters which can be grouped together or compared in order to provide a useful insight into the field.

When new students enter college, they often have similar questions – “Where to study for this subject?,” “How to prepare Data Structures and Algorithms?,” “How to connect with seniors?,” and so on. The use of chatbots can help them get answers to their questions quickly and efficiently. This study proposes a deep learning (DL) chatbot for addressing common doubts of university students, providing efficient and accurate responses to college-specific questions. A self-curated dataset is used for the purpose of building the chatbot, and natural language processing techniques are utilized for the pre-processing of raw data gathered. The study compares two deep learning models – a bidirectional long- and short-term memory network and a simple feed-forward neural network model. Received: 4 January 2023 | Revised: 9 May 2023 | Accepted: 9 June 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study.

To prevent a software from going obsolete, it is mandatory to update it in order to meet the current ever changing requirements. Each update is accompanied by a regression testing cost. Attempts to reduce this cost and effort have been made for over three decades now, but the search for a better technique continues. This paper offers an novel method to solve the regression test prioritization and selection issue in a nature inspired way using African Buffalo Optimization. A new ABO_TCSP algorithm has been proposed and explained with an example. The example has been tested using APFD metrics. The results prove to be optimal for the considered example and hence encourages the researchers to further validate and test the proposed approach.