Stability prediction of the software requirements specification

Sagrado, José del; Cano, Isabel María Del Águila

doi:10.1007/s11219-017-9362-x

Cited by 18 publications

(3 citation statements)

References 30 publications

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“…Examples are the exploration of the relationships between software metrics and defect proneness (distinguishing between the most and less effective metrics on defect proneness) [34], defect causal analysis [35], and code quality [36]. Other studies have focussed on assessing software quality, particularly considering the prediction of quality strategic indicators [37], and the requirements engineering phase [38][39][40]. They have also been used as a tool for making decisions in evidence-based [17] and value-based [41] SE.…”

Section: Bayesian Network Analysis In Software Engineeringmentioning

confidence: 99%

Bayesian Network analysis of software logs for data‐driven software maintenance

2023

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Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct datadriven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared. K E Y W O R D SBayes methods, software maintenance, software quality | INTRODUCTIONSoftware quality management is a key activity for the maintenance of software systems. Indeed, the budget that companies invest in software quality management is estimated to increase in the next years [1]. Software systems can generate large amounts of data both from its internal state and from how it is being used. The maintenance of such systems can benefit from the analysis of the available data to get better insights of the system, thus, performing data-driven software maintenance. For instance, companies can analyse the user experiences of their software systems [2] in order to detect possible defects, such as performance downgrades or unexpected errors. To improve the user experience, it is key to understand the users' usage patterns when navigating a software system. Understanding these patterns enables companies the ability to enhance the design of their software systems [3] as well as provide personalised information depending on the user [4]. In this regard, software logs are a popular resource to monitor.Developers place Application Programming Interface calls to a logging framework (e.g., SLF4J [5] or Log4J [6]) to record events that describe the internal state of the system as it runs in production. The analysis of software logs has been proved useful in improving system comprehension [7,8], performance analysis [9, 10], finding usage patterns [11,12], and anomaly detection [13,14]. Nevertheless, this analysis is not free from its own challenges [15].Regarding software logs analysis, the introduction of Bayesian Networks (BNs) based on behaviour data to improve performance and users' experience has not been ...

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Section: Bayesian Network Analysis In Software Engineeringmentioning

confidence: 99%

Bayesian Network analysis of software logs for data‐driven software maintenance

2023

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“…It is a widely studied data set within the domain of Requirements Engineering that has been used in several works with many different approaches, such as the use of exact methods in NRP [35], the study of requirements interactions [39], clustering technique for prioritization [1] or the extraction from the RALIC data set of the values to be included in a Bayesian network that predicts the stability of a software requirements specification [33]. The importance of stakeholders in RALIC data set has also been studied using genetic algorithms [21] or clustering [38].…”

Section: Replacement Access Library and Id Card Project Data Setmentioning

confidence: 99%

Salience Based Stakeholders Selection Keeping Coverage in the Next Release Problem Solution

Cano

Sagrado

2022

SSRN Journal

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“…Researchers indicate that 40-60% of software development project failures originate in requirements specification [1]. Requirements dictate how the product is designed and implemented in the following stages [2]. Overlooking software requirements during the requirements specification process can cause future threats and failures in the operational phase [3].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model

Siahaan¹,

Darnoto²

2022

Preprint

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Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

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Stability prediction of the software requirements specification

Cited by 18 publications

References 30 publications

Bayesian Network analysis of software logs for data‐driven software maintenance

Bayesian Network analysis of software logs for data‐driven software maintenance

Salience Based Stakeholders Selection Keeping Coverage in the Next Release Problem Solution

A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model

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