Improving Software Requirements Prioritization through the Lens of Constraint Satisfaction

In a company producing off‐the‐shelf software for mass markets, the future development steps of the products cannot be negotiated with one or few customers. The decisions concerning the priorities of the requirements must be made within the company, the developer bearing all the financial risks included. This means that finding the right priorities for the requirements is important. However, requirements prioritization is recognized as a difficult activity in software product development. The literature offers methods for requirements prioritization, but many authors report that practices in companies are mostly informal. In this study, we evaluated two requirements prioritization methods in industrial product development projects. In the first case, the users of the system evaluated thepair‐wise comparison techniquefor prioritizing user needs. In the second case, practitioners evaluatedWiegers' methodfor change requests. In addition, we interviewed 11 practitioners from 6 companies about their current requirements prioritization practices and the models that they use as a basis of their prioritization decisions. Our findings indicate that prioritization methods may have limited ability to support decision‐making in a complex area like requirements prioritization in market‐driven product development. In addition, there are practical difficulties in the usage of methods, and therefore, prioritization results should be taken more as being indicative than as an ultimate truth. Copyright © 2006 John Wiley & Sons, Ltd.

Test case prioritization technique aims to improve test efficiency rate by sorting test cases according to some specific criteria. Requirements play an important role throughout software testing. This paper proposes a test case prioritization method based on requirement correlations. Prioritization of requirements is defined by the users and the developers. This technique focuses on requirements with detected faults after the last regression testing. By readjusting prioritization of fault-related requirements, it can optimize the order of test cases. Experimental results show that this technique exactly contributes to achieving high testing efficiency.

Traditional methods for requirements prioritization (RP) are currently limited by scalability and lack of automation issues. In recent years, there has been an exponential growth in the use of Artificial Intelligence (AI) techniques in different areas of software engineering (e.g., requirements analysis, testing, maintenance). In particular, I have found thirteen RP methods applying AI techniques such as machine learning, or genetic algorithms. 38% of these approaches seek to improve the scalability problem, whereas only 15% of them aim to improve the automation aspect along the RP process. Moreover, all these studies have carried out their evaluations with a number of requirements no greater than 100.In order to address the issues of scalability and lack of automation in RP, the present research project aims to propose a semi-automatic multiple-criteria prioritization method for functional and non-functional requirements of software projects developed within the Software Product-Lines paradigm. The proposed RP method will be based on the combination of Natural Language Processing techniques and Machine Learning algorithms, and for its validation, empirical studies will be carried out with real web-based geographic information systems (GIS). This paper describes the problem and technical challenges to be addressed, the related works, as well as the main contributions of the proposed solution.

This paper presents a novel requirements prioritization (RP) method which complements ad-hoc ranking approaches and ordinal scale-based RP techniques with capabilities of handing the vague and subjective perceptions that stakeholders have when they rank candidate requirements. To consider the indeterminacy and the lack of knowledge of stakeholders, the suggested method represents requirements' rankings with Intuitionistic Fuzzy Sets. The final priority list of requirements with respect to the prioritization criteria is derived by applying a multi-criteria decision method that is based on objective criteria weights. The larger the hesitation associated with a requirements ranking according to a chosen criterion, the smaller would be the weight of this criterion for the calculation of the final priority list. Through a case study, we demonstrate that the method is suitable for prioritizing requirements in an agile development process.1

Pairwise comparison is a popular method for decision making. Analytic Hierarchy Process (AHP) is based on pairwise comparison (PC), which the rating results of PC forms pairwise reciprocal matrices (PRM) further to be derived as the priority values by the Eigen method. Prioritization method using Eigen method is still open to discuss, although many applications using this method in AHP have been made. This study proposes the Canonical Particle Swarm Optimized Direct Least Squares Prioritization (CPSO-DLSP) method for deriving the priority values from a PRM. The direct least squares function leads to the least total square variance between ground PRM and the derived priority values. The Canonical Particle Swarm Optimization (CPSO) algorithm is one of the promising methods to effectively solve the direct least squares optimization problem. An application of mutual fund selection using the conventional AHP method is revised and demonstrated by the proposed method.

Background: Requirement prioritization plays key role in software development process. It is essential to prioritize the requirements for making the correct decision for either a single or multiple release of a product. In this paper we performed a systematic analysis on some of the significant factors like importance of requirements, risks, cost and time in context of requirement prioritization. Objective: With benefits these prioritization methods also have some limitations and shortcoming that are brought up in this paper. Stakeholders, managers, developers or their representatives make decisions for prioritization of requirements. Many techniques are analyzed how to manage these prioritizations considering general goals and limitations. Method: For the identification and analyzing of research articles published during 2009-2017, Systematic Literature Review based method is used in this paper. Results: In recent researches 40 different requirement prioritizations techniques has been used. This SLR also shows the major research gaps regarding techniques and tools for software requirements prioritization. Conclusion: This research shows the major prioritization techniques and tools for requirements elicitation. Tools & techniques identified in this study will assist future researchers to expend their views in the context. Moreover, it will help requirement engineers and practitioners to choose requirement prioritization techniques and tools according to their needs.

Requirements prioritization is one of the key factors in deciding the success of the project and hence the software industry. One of the major concerns in software prioritization techniques is that the existing ranking techniques have a very modest support to different criteria used by stakeholders to present their ranking. The current techniques are not suitable for arriving at anoptimized view of multiple stakeholders using multiple criteria. This research analyzes the issues in existing techniques. A web based decision support model using ELECTRE as the method for prioritization is proposed. ELECTRE is a multi-criteria decision making model that is proved to be effective in ranking several decision making problems. The proposed system takes input from multiple stakeholders using 100-point method. An optimized ranking is obtained using ELECTRE method. The developed system is validated using a pilot project and is found to be efficient in terms of saving cost of implementation and man-hours needed for implementation.

The validity of the priority vector used in the analytic hierarchy process (AHP) relies on two factors: the selection of a numerical scale and the selection of a prioritization method. The traditional AHP selects only one numerical scale (e.g., the Saaty scale) and one prioritization method (e.g., the eigenvector method) for each particular problem. For this traditional selection approach, there is disagreement on which numerical scale and prioritization method is better in deriving a priority vector. In fact, the best numerical scale and the best prioritization method both rely on the content of the pairwise comparison data provided by the AHP decision makers. By defining a set of concepts regarding the scale function and the linguistic pairwise comparison matrices (LPCMs) of the priority vector and by using LPCMs to unify the format of the input and output of AHP, this paper extends the AHP prioritization process under the 2-tuple fuzzy linguistic model. Based on the extended AHP prioritization process, we present two performance measure criteria to evaluate the effect of the numerical scales and prioritization methods. We also use the performance measure criteria to develop a 2-tuple fuzzy linguistic multicriteria approach to select the best numerical scales and the best prioritization methods for different LPCMs. In this paper, we call this type of selection the individual selection of the numerical scale and prioritization method. We also compare this individual selection with traditional selection by using both random and real data and show better results with individual selection.

Requirements prioritization (RP) is a crucial process which aims to evaluate candidate software requirements to be implemented in the next release of a software system. RP is often applied iteratively, according to various criteria, by multiple stakeholders, who may have different roles and needs. The problems encountered during the RP of large sets of requirements can be addressed by following a systematic large-scale decision-making (LSDM) process. The aim of this chapter is to propose an LSDM RP process which applies multi-criteria evaluation based on intuitionistic fuzzy sets (IFSs). IFSs are suitable to consider stakeholders hesitation for criteria importance and requirements ratings. The proposed process is supported by a recommender system which provides suggestions to stakeholders and also guide them to collaboratively reach consensus for the final RP. The proposed process is tested using an illustrative example. The results are promising since they demonstrate that the suggested process can effectively support multiple stakeholders to prioritize large requirements sets.

The objective of the study is to develop an algorithm for prioritizing requirements in the development of software for critical infrastructure object projects. Requirement development is a fundamental phase in any software project, as this phase involves the identification, processing, and manipulation of requirements. The primary source of these requirements is project stakeholders, taking into account project constraints and limits. The number of requirements varies for each software project for a critical infrastructure object, hence the term requirement prioritization pertains to determining the priority order of executing software requirements based on considerations and decisions of stakeholders. Various proposed optimization algorithms are employed to address optimization tasks. This paper presents the main stages of basic optimization algorithms, some of their modifications aimed at enhancing their efficiency in solving such types of problems. Additionally, a hybrid approach based on WOA and GWO optimization algorithms is proposed, combining the advantages of each algorithm to determine the priority of requirements for critical infrastructure object software. Furthermore, a dataset from the SKUDA project is provided, utilized in this research, meeting the requirements of a real software project for evaluating the proposed method. The scientific novelty lies in the modification, application, and combination of results from well-known GWO and WOA algorithms to address the requirement prioritization task for critical infrastructure object software projects. The proposed algorithm achieves an accuracy of 92% for the proposed set of requirements.

This paper proposes a dependency based collaborative requirement prioritization method which takes into account multiple criteria's for obtaining individual preference in the form of initial ranking from both stakeholders and developers. The proposed approach is an effective mechanism to extract relevant knowledge in terms of initial priority given by stakeholders and analysis of requirement inter relationships expressed as the initial priority from developers and final ranking computed through a weighted score.

The increasing size of requirements is challenging to requirement prioritization in a large scale software engineering projects. Most existing requirement prioritization methods have high accuracy but suffer a problem of scalability. To overcome the aforementioned shortcomings, this paper proposes a prioritization approach based on Requirement digraph (R-digraph). Via a transformation from requirements to nodes and from prioritization relationships to edges, we propose one algorithm to build the R-digraph with less human interactions, and another algorithm is designed to perform the dominance test and to output the final rank of requirements. A comprehensive experiment is conducted to evaluate the performance of our proposal. The results show that our approach achieves reliable performance in terms of less human interaction than the state-of-the-art.

The chapter gives a hybrid approach for prioritization of software requirements by considering regression testing efforts as one of the criteria. The prioritization is performed against the prioritized decision aspects. The chapter gives software engineers a technique for prioritizing both requirements and aspects. This technique considers regression testing effort as one of important parameters during requirement prioritization thereby reducing the regression testing effort. This technique is applied on live web application of “virtual classroom” and the results were promising. The technique resulted in decision aspect prioritization thereby leading to effective requirement prioritization and lower regression testing efforts.

The goal of any automated requirement prioritization process is to reduce the difference between the true and estimated ranking. It is arguable that to what extent, using stakeholders' feedback will reduce that difference. Moreover, incorporating requirement ranking functions such as value, cost, risk, etc. along with the feedback is another concern. This paper proposes a scheme for requirement prioritization addressing the feedback issue along with a highly significant ranking function that have decisive impact on true ranking. The approach initially creates partial orders on the basis of stakeholders' feedback and then merges those orders using binary search technique. Two variations of proposed method were implemented varying the number of elicited requirement pairs which were ordered based on stakeholders' preference. The variations were analyzed on the basis of displacement for each requirement from true to estimated rank. Results indicate that for projects having significant ranking functions, decreasing stakeholders' feedbacks could help in reducing the ranking difference.

Nowadays, it is impossible to ignore the implementation of security features in information systems since they manage important assets that are critical for the business processes of organizations. In this aspect, there have been several researches for introducing the security analysis in different stages of software development life cycle. Among those solutions, one of the most interesting one is the usage of misuse cases. Misuse cases, which are extensions of the well-known use cases, were created for defining security requirements. A misuse case can be considered as the inverse of a use case and it defines functions that the system should not allow. Even though, misuse cases are very useful for eliciting security requirements, they do not provide a mechanism to prioritize such requirements. Therefore, they do not address the problem of optimal risk management. Software engineers often have to work within a given set of budget constraints that may impede them from implementing all possible countermeasures. Thus, the software engineer needs to find a way to prioritize the security requirements to decide which requirements will be developed. Motivated by the mentioned limitation of misuse cases, the presented paper proposes an enhanced misuse case model which incorporates a method for prioritization of security requirements.