Software Requirements Specification Research Articles

Generating automated test case from sequence diagram using Pre-order Traversal

Automated test case generation is important in software testing to evaluate developed software. The testing process requires test cases that can be based on Software Requirements Specification (SRS) and Software Design Document (SDD). One of UML diagrams included in the previous documentations is sequence diagram. This research proposed a modified pre-order algorithm that can generate test cases automatically from sequence diagrams. Our manual cyclomatic complexity evaluation showed that our method produced accurate test cases based on the sequence of messages in the sequence diagram. This result justifies that the generated test cases match the relevant sequence diagram.

Early analysis of requirements using NLP and Petri-nets

Scenario-based approaches are widely used for software requirements specification. Since scenarios are usually written using natural language, specifications may have statements that are ambiguous, unnecessarily complicated, missing, duplicated, or conflicting. Requirements quality is challenging since it is hard to achieve consistency in requirements products. Unfortunately, if done manually, analysis of textual scenarios can be an arduous, time-consuming, and error-prone activity. This work rethinks the unambiguity, completeness, consistency, and correctness properties of scenario-based specifications; and how static and dynamic analysis strategies could automatically evaluate them. To do so, we introduce an automated requirements analysis approach to check both structural and behavioral aspects of scenarios, which combines natural language processing, Petri-nets, and visualization techniques for: (i) identifying certain types of defects and their indicators; (ii) highlighting scenario statements or relationships among scenarios that can lead to defects; and (iii) foreseeing scenario execution paths that can lead to inconsistencies. We show the feasibility of the proposed approach through the analysis of four projects specified as scenario-based descriptions. Overall, our approach produced reasonable results, with precision greater than 89% and recall greater than 98%. Our work allows researchers, as well as practitioners, to improve the quality of scenarios through an automated analysis approach.

Analysis of System Requirements and Architecture for Facilitating Table-Based Data Clustering for Non-Technical Users


 
 
 
 Clustering is one of the key techniques in unsupervised learning analysis, aimed at grouping similar data objects into clusters based on shared characteristics. The broad benefits of clustering are evident across various sectors, such as business, marketing, finance, and many others. However, the complexity of implementing clustering, especially for those without a background in statistics or programming, poses a barrier. The appropriate selection of clustering methods and accurate interpretation of results require a solid understanding of statistics. This research aims to address this issue by crafting a detailed Software Requirements Specification for a user-friendly clustering application, equipped with an intuitive interface and effective tools, based on comprehensive literature study, which finally allowing non-experts to engage in the clustering process without in-depth knowledge of statistics or programming. As such, this study endeavors to provide a practical solution for utilizing clustering without excessive technical impediments.
 
 
 

GoEkopz: An E-Koperasi and Marketplace Synergy of Koperasi MSMEs Model Platform - Case Study: Koperasi Giat, eKopz Startup, PPKM Community

With a population of 273.5 million in 2020, the people's economy is very important for Indonesia. The populist economic model has existed in Indonesia for a long time, locally known as “Koperasi”. However, Koperasi could not keep up with information and technology in the industrial 4.0 era. When other industrial models adopt information technology massively, Koperasi seems to struggle to shift from the conventional model. Slowly but surely, Koperasi become less popular, especially among Generation Z, who considers information technology part of their daily needs and lifestyle. The scope of Koperasi’sbusiness shrinks and becomes limited, along with their business capital. On the other hand, information technology provides opportunities for small and medium-sized entrepreneurs. The data shows that this sector contributes to 60.5% of the national GDP, absorbs 96.9% of the workforce, and provides 99.9% of total employment. Unfortunately, many Micro, Small, and Medium Enterprises (MSME) have limited capital and tend to prefer fintech services that offer easier accessibility than Koperasi. This paper aims to propose an E-Koperasi and the synergy of Koperasi and MSMEs model platform, specifically the digital marketplace platform. We design the platform requirements using the Software Requirement Specification with the User Acceptance Test. As a result, an application is developed as a model platform available for Koperasi and MSMEs. The platform is proposed to support the Government’s program to digitalize the Koperasi and the MSMEs and increase their competitiveness in Industrial 4.0.

Extracting software requirements-related information from online news using DomText-WMDS

Currently, there are not many studies that assess software requirements extraction from non-software artifacts. Most of the research in these related areas are focuses on software artifacts such as project descriptions or user reviews as a source of requirements extraction. This research aims to identify relevant information to the software requirements from online news using the vector space model. This software requirements-related information can assist systems analysts in discovering the problem domain based on the lesson learned presented by stakeholders in online news. This research proposes DomText-WMDS to extract requirements-related information from online news. We used online news and public software requirements specification dataset to develop software-specific vocabulary using domain specificity technique. Then we expanded the specific vocabulary software to obtain more comprehensive results by building vector space model from online news documents. This updated version of software-specific vocabulary can be used for basic filtering of software requirements-related information that previously extracted using the part-of-speech (POS) chunking. This study improved the performance for extracting software requirements-related information, with precision and recall 61.09% and 60.66% compared to domain specificity approach that only manages to obtain 43.34% and 40.78%.

Requirement Change Prediction Model for Small Software Systems

The software industry plays a vital role in driving technological advancements. Software projects are complex and consist of many components, so change is unavoidable in these projects. The change in software requirements must be predicted early to preserve resources, since it can lead to project failures. This work focuses on small-scale software systems in which requirements are changed gradually. The work provides a probabilistic prediction model, which predicts the probability of changes in software requirement specifications. The first part of the work considers analyzing the changes in software requirements due to certain variables with the help of stakeholders, developers, and experts by the questionnaire method. Then, the proposed model incorporates their knowledge in the Bayesian network as conditional probabilities of independent and dependent variables. The proposed approach utilizes the variable elimination method to obtain the posterior probability of the revisions in the software requirement document. The model was evaluated by sensitivity analysis and comparison methods. For a given dataset, the proposed model computed the low state revisions probability to 0.42, and the high state revisions probability to 0.45. Thus, the results proved that the proposed approach can predict the change in the requirements document accurately by outperforming existing models.

Vehicle Fixing Recommender System Using User-Based Collaborative Filtering Algorithm

The study refers to vehicle fixing support, an Online Application System for vehicle owners with vehicular problems guided by features on recommending service providers according to their geographical location and performance rating of service providers using the Collaborative Filtering Algorithm. As the number of vehicle owners increases, the tendency for services related to vehicle fixing and maintenance could also increase, providing the chance for vehicle service providers to be in the limelight in the digital world. The system stakeholders include the vehicle owners, vehicle service providers, and the technical support team that administers the registration and approval process among vehicle owners and service providers. Data management with transactions established are properly kept to track and trace services availed by vehicle owners served as the basis for collaborative filtering. The Front-end and back- end of the system are structured using HTML5, Bootstrap 4, CodeIgniter4, JavaScript, and MySQL for its database. The system was found to have advertised the service providers’ skills and services to the customers as vehicle owners who search for and therefore book an appointment with the available service providers online. The desired results were met when the transaction is done from booking appointments through the system to successful services offered on-site. The search results had shown the recommended service providers that precisely fit the vehicle owner's or driver's search parameters criteria. The system's availability has been concluded which obtained a rating of 5 with the quality description of excellent functionality by the end-user reviews by the software requirements specifications

Fault-Prone Software Requirements Specification Detection Using Ensemble Learning for Edge/Cloud Applications

Ambiguous software requirements are a significant contributor to software project failure. Ambiguity in software requirements is characterized by the presence of multiple possible interpretations. As requirements documents often rely on natural language, ambiguity is a frequent challenge in industrial software construction, with the potential to result in software that fails to meet customer needs and generates issues for developers. Ambiguities arise from grammatical errors, inappropriate language use, multiple meanings, or a lack of detail. Previous studies have suggested the use of supervised machine learning for ambiguity detection, but limitations in addressing all ambiguity types and a lack of accuracy remain. In this paper, we introduce the fault-prone software requirements specification detection model (FPDM), which involves the ambiguity classification model (ACM). The ACM model identifies and selects the optimal algorithm to classify ambiguity in software requirements by employing the deep learning technique, while the FPDM model utilizes Boosting ensemble learning algorithms to detect fault-prone software requirements specifications. The ACM model achieved an accuracy of 0.9907, while the FPDM model achieved an accuracy of 0.9750. To validate the results, a case study was conducted to detect fault-prone software requirements specifications for 30 edge/cloud applications, as edge/cloud-based applications are becoming crucial and significant in the current digital world.

An Automatic Generation and Verification Method of Software Requirements Specification

The generation of standardized requirements specification documents plays a crucial role in software processes. However, the manual composition of software requirements specifications is a laborious and time-consuming task, often leading to errors that deviate from the actual requirements. To address this issue, this paper proposes an automated method for generating requirements specifications utilizing a knowledge graph and graphviz. Furthermore, in order to overcome the limitations of the existing automated requirement generation process, such as inadequate emphasis on data information and evaluation, we enhance the traditional U/C matrix by introducing an S/U/C matrix. This novel matrix represents the outcomes of data/function systematic analysis, and verification is facilitated through the design of inspection rules. Experimental results demonstrate that the requirements specifications generated using this method achieve standardization and adherence to regulations, while the devised S/U/C inspection rules facilitate the updating and iteration of the requirements specifications.

Element quality indicator: A quality assessment and defect detection method for software requirement specification

A software requirements specification (SRS) provides a detailed description of the requirements of a software system that is to be developed. The Element Quality Indicator (EQI) is a novel method to detect defects and assess the quality of an SRS. It does not hinge on review guidelines and is based on the SRS element questions extraction method (EQEM). In this study, we optimized the EQI and carried out a systematic and comprehensive experiment to validate and evaluate its effectiveness. The controlled experiment, which included 60 software engineering students, found that 100% of the subjects identified defects in the SRS using EQI. Moreover, the results indicated that the average number of defects detected with EQI was greater than that of the classical review technique - perspective-based reading. Furthermore, the controlled experiment demonstrated that EQI provides a comparatively objective and accurate evaluation of the quality of the SRS and markedly diminishes the bias in understanding software requirements due to the ambiguity of natural language.

Mobile Application Software Requirements Specification from Consumption Values

In today’s society, mobile applications are becoming more popular and providing several advantages. However, users will resist using a product regardless of how well-tested or solid it is if the wrong requirements are implemented. Understanding the factors that influence the purchase of mobile applications can provide useful information for mobile application design and development. Hence, the purpose of this research is to better understand the impact of consumption values on customers in order to identify the software requirements for a mobile application. This study analyzes the possible behavioral changes of similar groups of university students in a five-year period. For this purpose, a questionnaire is administered to engineering faculty students in 2017 (46 females and 66 males) and 2021 (45 females and 90 males) to better understand customer behavioral changes. The findings highlight the significance of conditional value in customer behavior when purchasing mobile applications. Even though the other consumption values were found to have a negligible effect, there is some evidence indicating that the impact of consumption values on different target customer groups may vary considering their gender and familiarity with apps. Further research needs to be conducted to better understand the possible impact of age, cultural differences, education levels, and special considerations such as visually impaired people. Therefore, this study encourages mobile application designers and developers to raise awareness for the effect of consumption values such as conditional value on their customers’ mobile application purchasing behaviors. The possible impact of the consumption values needs to be deeply understood, specifically for the target customer groups, and it should be considered in the software requirements specification (SRS), which is one of the important principles that allow software under consideration for development to function. As a result, a better understanding of consumption values will help developers design and develop better applications by specifying software requirements and marketing strategies.

Method for forecasting the level of software quality based on quality attributes

The paper develops the method for forecasting the level of software quality based on quality attributes. This method differs from the known ones in that it provides forecasting the quality level of future software based on the processing the software quality attributes’ values, which are available in the software requirements specification (SRS). So, the proposed method makes it possible to compare the SRSs, to immediately refuse the realization of a software based on unsuccessful SRS (saving money and time, reducing the probability of failed and challenged projects), and to make a reasonable choice of the specification for the further implementation of a software with the highest quality (of course, if errors will not be introduced at subsequent stages of the software life cycle). During the experiments, 4 SRS were analyzed, which were fulfilled by different IT firms of Khmelnytskyi (Ukraine) for the solution of the same task. Taking into account the forecasted quality level of the future software, which will have developed according to each of the analyzed SRS, a comparison of the 4 analyzed SRS was made, and a reasoned choice of the specification was made for the further realization of the highest quality software.

What Should a Good Software Requirements Specification Include? Results of a Survey

Abstract Software requirements specification is an important foundation of the software development process. It documents the requirements, expectations, and restrictions for a system to be developed. It should describe what the produced system will offer in detail and unambiguously. It should also provide a realistic basis for estimating product costs, risks, and schedules. Still, it is difficult to prepare a useful specification effectively. We want to propose an approach to creating a good specification. We started the work from scratch by determining what the scope of the specification should be. We asked people who work on IT projects what information is and should be included in the specifications with which they work. This paper presents results from a survey conducted with 163 participants who have experience working on commercial software development projects. The main observation is that the content of requirements specification differs with respect to project characteristics, such as industry or financing method. We also noticed that the information about integration with external systems and functionalities most often appears in the SRS.

Design and Development of the e-Review

Requirements review is a formal review process in which several reviewers read all or part of software requirement specification (SRS) and search for defects. In order to ensure that the system requirements have been completely and clearly identified, reviewers use many different review techniques. Despite of the availability of many reading techniques such as perspective-based reading techniques, checklist-based reading techniques and a few more, we could not find any existing tool to support two or more reading techniques in order to guide reviewer while reviewing a document. Besides, currently, in order for the reviewers to review an SRS document, they need to gather physically, make a session and give their review feedback during the session. In such cases, the requirement engineers need to schedule the review session based on the availability of the reviewers. The review leader needs to manually organize the session and review outcomes also need to be manually consolidated. Thus, the objectives of this project are to develop a web-based tool in order to; (1) enable reviewers to review SRS document using two different reviewing techniques such as checklist-based reading (CBR) and perspective-based reading (PBR); (2) enable review leader to assign reviewer for requirement review; and (3) allow compilation of the review feedback to be generated. The project applied Iterative process model to manage the software development process. The implementation of the project is using PHP JavaScript, HTML, CSS, Bootstrap, JQuery, and Ajax

ReqGen: Keywords-Driven Software Requirements Generation

Software requirements specification is undoubtedly critical for the whole software life-cycle. Currently, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to speed up the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task, which needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen, which can provide further assistance by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from the domain ontology and is injected into the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirements-syntax-constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords’ (phrases’) inclusion, BLEU, ROUGE, and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements.

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development, a software requirements specification (SRS) is essential, and whether the requirements are clear and explicit is the key. However, due to various reasons, there may be a large number of misunderstandings. To generate high-quality software requirements specifications, numerous researchers have developed a variety of ways to improve the quality of SRS. In this paper, we propose a questions extraction method based on SRS elements decomposition, which evaluates the quality of SRS in the form of numerical indicators. The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects, especially the description problem and omission defects in SRSs. To verify the effectiveness of the proposed method, we conducted a controlled experiment to compare the ability of checklist-based review (CBR) and the proposed method in the SRS review. The CBR is a classic method of reviewing SRS defects. After a lot of practice and improvement for a long time, CBR has excellent review ability in improving the quality of software requirements specifications. The experimental results with 40 graduate students majoring in software engineering confirmed the effectiveness and advantages of the proposed method. However, the shortcomings and deficiencies of the proposed method are also observed through the experiment. Furthermore, the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

Structured SRS for e-Government Services With Boilerplate Design and Interface

There are many projects being carried out to develop e-Government applications. In order to develop an efficient application, a proper requirements specification is strictly required. However, in the case of improper requirements specifications, errors are unavoidable for the developed applications. Therefore, it is necessary to use a model to determine the quality of software requirements. In the literature, several studies have been presented to propose a quality model for software requirements. Yet, there is no study on the development of a quality model for software requirements in Turkish language. Thus, the purpose of this study is to propose a quality assessment model of Software Requirements Specification (SRS) in Turkish language for e-Government applications. The proposed model aims at defining common texts and confirming that the sentences used in writing SRS are developed within an assured structure in order to minimize and standardize the errors originating from natural language. For this purpose, a model based on the Rupp’s boilerplate is created that allows the analyst or requirements engineer to accurately enter the requirements statements. In this study, an interface (webpage) is also created so that the model may match requirements with common text templates, compute similarity values, and also may insert the requirements, regulatory documents, and user requirements into the template in a more convenient format. In order to evaluate the proposed model, the sentences in the 32 documents, including 843 requirements, were adapted to the model. Then, the usability of the model was validated by the requirement engineers serving in a Government service. According to the results, it is concluded that the proposed model is applicable, and is able to improve the quality of SRS in Turkish language for e-Government applications.

An Approach towards Goal-Oriented Requirements Ontology: Consistency and Completeness Based Requirements Analysis

The paper presents a new approach to managing software requirement elicitation techniques with a high level of analyses based on domain ontology techniques, where we established a mapping between user scenario, structured requirement, and domain ontology techniques to improve many attributes such as requirement consistency, completeness and eliminating duplicate requirements to reduce risk of overrun time and budgets. One of the main targets of requirement engineering is to develop a requirement document with high quality. So, we proposed a user interface to collect all vital information about the project directly from the regular user and requirement engineering; After that, the proposal will generate an ontology based on semantic relations and rules. Requirements Engineering tries to keep requirements throughout a project’s life cycle consistent necessities clear, and up to date. This prototype allows mapping requirement scenarios into ontology elements for semantically interrupted. The general points of our prototype are to guarantee the identification requirements and improved nature of the Software Requirements Specification (SRS) by solving incomplete and conflicting information in the requirements specification.

An Automated Impact Analysis Approach for Test Cases based on Changes of Use Case based Requirement Specifications

Change Impact Analysis (CIA) is essential to the software development process that identifies the potential effects of changes during the development process. The changing of requirements always impacts on the software testing because some parts of the existing test cases may not be used to test the software. This affects new test cases to be entirely generated from the changed version of software requirements specification that causes a considerable amount of time and effort to generate new test cases to re-test the modified system. Therefore, this paper proposes a novel automatic impact analysis approach of test cases based on changes of use case based requirement specification. This approach enables a framework and CIA algorithm where the impact of test cases is analysed when the requirement specification is changed. To detect the change, two versions as before-change and after-change of the use case model are compared. Consequently, the patterns representing the cause of variable changes are classified and analysed. This results in the existing test cases to be analysed whether they are completely reused, partly updated as well as additionally generated. The new test cases are generated automatically by using the Combination of Equivalence and Classification Tree Method (CCTM). This benefits the level of testing coverage with a minimised number of test cases to be enabled and the redundant test cases to be eliminated. The automation of this approach is demonstrated with the developed prototype tool. The validation and evaluation result with two real case studies from Hospital Information System (HIS) together with perspective views of practical specialists confirms the contribution of this tool that we seek.

A multi‐agent K‐means with case‐based reasoning for an automated quality assessment of software requirement specification

AbstractAutomating the quality assessment of Software Requirement Specification poses major challenges related to the need for advanced algorithms to extract the SRS quality features, interpret the context of the features, formulate accurate assessment metrics, and document the shortcomings as well as possible improvements. In the existing methods, such as Reconstructed Automated Requirement Measurement, and Rendex, some major processes are still handled offline by humans (semi‐automated) or encompass automating the measurement of a few quality attributes due to the mentioned challenges. This paper addressed this gap and proposed an Automated Quality Assessment of SRS (AQA‐SRS) framework to assess the SRS documents by automatically extracting features related to 11 quality attributes through a deep analysis of the SRS textual content. Also, it constructs a flexible platform that is able to minimize the human expert’s role in the SRS assessment. The AQA‐SRS framework integrates Natural Language Processing, K‐means, Multi‐agent, and Case‐Based Reasoning. The AQA‐SRS framework is evaluated by processing two standard SRS datasets and comparing the results with state‐of‐the‐art methods and analysis by software engineering experts. The results show that the AQA‐SRS framework effectively assesses the tested SRS documents and achieves a 78% total agreement with the tested methods and software engineering experts.