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Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.

Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to identity and exclude redundant, similar and faulty test cases from the given regression suite. Our experimental results clearly show the benefits of the ETCPM framework in terms of reduction in the testing time, optimization of the resource allocation, and improvement in the overall quality of regression test suite. ETCPM enables software development teams to achieve faster and reliable regression testing by intelligent exclusion of similar and fault test cases, which yields in reduction in the software delivery cycles and better end user satisfaction.

The use of text data with high dimensionality affects classifier performance. Therefore, efficient feature selection (FS) is necessary to reduce dimensionality. In text classification challenges, FS algorithms based on a ranking approach are employed to improve the classification performance. To rank terms, most feature ranking algorithms, such as the Relative Discrimination Criterion (RDC) and Improved Relative Discrimination Criterion (IRDC), use document frequency (DF) and term frequency (TF). TF accepts the actual values of a term with frequently and rarely occurring terms used in existing feature ranking algorithms. However, these algorithms focus on the number of terms in a document rather than the number of terms in the category. In this research, an alternative method to RDC, called Alternative Relative Discrimination Criterion (ARDC) was proposed, which aims to improve the accuracy and effectiveness of RDC feature ranking. Specifically, ARDC is designed to identify terms commonly occurring in the positive class. The results obtained were compared to the existing RDC methods, which are RDC and IRDC, and standard benchmarking functions such as Information Gain (IG), Pearson Correlation Coefficient (PCC), and ReliefF. The experimental results reveal that using the suggested ARDC on the Reuters21578, 20newsgroup, and TDT2 datasets provides better performance in terms of precision, recall, f-measure, and accuracy when employing well-known classifiers such as multinomial naïve Bayes (MNB), Support Vector Machine (SVM), Multilayer perceptron (MLP), k-nearest neighbor (KNN), and decision tree (DT). Another experiment was performed to validate the proposed technique, which aims to showcase the novelty of the ARDC approach. The experiment utilized the 20newsgroup dataset and employed the Relevant-Based Feature Ranking (RBFR) technique. Naïve Bayes (NB), Random Forest (RF) and Logistic Regression (LR) classifiers were used in this experiment to demonstrate the effectiveness of the suggested ARDC.

Ambiguous user requirements are usually considered problematic in software engineering. Therefore, many studies have been conducted on its avoidance and detection. However, the detected ambiguities were resolved manually using interviews, brainstorming, and group discussion sessions among the elicitors and stakeholders for whom the software was developed. If not addressed efficiently, it gives rise to the explicit issues of additional time and cost involved and the stakeholders’ availability to clarify them during multiple sessions. However, if appropriately addressed, it can reveal some implicit issues, such as tacit knowledge, hesitation, and terminological discrepancies. Identifying these implicit issues is not easy, as it requires expert elicitation skills that usually come with experience. In addition to the increasing demand for an automated approach to address these implicit issues, the recent COVID 19 pandemics has also amplified the demand to address the explicit issue of stakeholder availability. This paper proposes an implementable semi-automated approach to help elicitors address these demands. The proposed approach uses intuitionistic fuzzy logic to address hesitation and statistical functions to identify discordance and tacit knowledge. It also uses the heuristic knowledge gained in each iteration to improve itself. We implemented it in an online tool and conducted controlled experiments to evaluate our approach, and the results were compared. We achieved precision, recall, and F1 score of 0.769, 1, and 0.869, respectively, during our experiments. The results show that the proposed approach may minimize the explicit issues and help novice elicitors address the implicit issues discussed earlier.

Mobile applications are expected to receive context input Mobile applications are expected to receive context in put such as location, speech, and network from different context providers. Since context can be considered as knowledge, a formal method is needed to capture this knowledge. There isless work on ontology model that could be reused to model a new context ontology for Android mobile application. Therefore, this study proposed an ontology specifically for Android mobile application, COCCC, to formalize context knowledge present within it. METHONTOLOGY method was used to create COCCC ontology as it offers intermediate representation in the form of concepts. The concepts from the context ontology were extracted from various resources, sorted and categorized based on types and functions for standardization purposes. Survey was given to five domain experts for evaluation of COCCC ontology in terms of its usability. Data from these experts were analyzed and the results have confirmed that the proposed context ontology is usable to Android mobile application developers.

Mobile applications are expected to receive context input Mobile applications are expected to receive context in put such as location, speech, and network from different context providers. Since context can be considered as knowledge, a formal method is needed to capture this knowledge. There isless work on ontology model that could be reused to model a new context ontology for Android mobile application. Therefore, this study proposed an ontology specifically for Android mobile application, COCCC, to formalize context knowledge present within it. METHONTOLOGY method was used to create COCCC ontology as it offers intermediate representation in the form of concepts. The concepts from the context ontology were extracted from various resources, sorted and categorized based on types and functions for standardization purposes. Survey was given to five domain experts for evaluation of COCCC ontology in terms of its usability. Data from these experts were analyzed and the results have confirmed that the proposed context ontology is usable to Android mobile application developers.