Forecasting software development project characteristics using meta-modeling

Bazhenov, A. S.; Itsykson, Vladimir

doi:10.1145/2556610.2556614

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…2. Human factors (1). Is software accuracy highly derived from precision in software success/failure rate estimation?…”

Section: High-level Goals and Research Questionmentioning

confidence: 99%

“…The predictability of software success is one of the major concerns in the software industry. To predict or forecast software success or failure, one can use different software development methods and tools, commonly used in the software development industry [1]. A considerable body of knowledge on the software defect prediction links cost-saving and different phases of the "software development life cycle" (SDLC).…”

Section: Introductionmentioning

confidence: 99%

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Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Hasnain¹,

Ghani²,

Jeong³

et al. 2023

Computers, Materials &Amp; Continua

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Forecasting on success or failure of software has become an interesting and, in fact, an essential task in the software development industry. In order to explore the latest data on successes and failures, this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework? What human factors contribute to success or failure of a software? What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work? In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors, misinterpretation, and miscommunication of requirements and decision-making processes play their roles in software success forecasting. We discussed a potential relationship between forecasting of software success or failure and the development processes. We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study, and they were more likely to rate software success forecasting linking to the development processes. Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate. The results of this empirical study also validated that requirements' misinterpretation and miscommunication were the main causes behind software systems' failure. It has been seen that reliable, relevant, and trustworthy sources of information help in decision-making to predict software systems' success in the software industry. This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects. Future investigation can be performed to identify the other human factors that may impact software systems' success.

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“…2. Human factors (1). Is software accuracy highly derived from precision in software success/failure rate estimation?…”

Section: High-level Goals and Research Questionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Hasnain¹,

Ghani²,

Jeong³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Metrics for software process simulation modeling

Liu,

Zhang,

Dong

et al. 2024

J Software Evolu Process

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Software process simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. We aim to provide evidence‐based support of the process metrics for software process (simulation) modeling. A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following our proposed ontology of metrics in SPS. We identify 131 process modeling studies that collectively involve 1975 raw metrics and classified them into 21 categories using the coding technique. We found product and process external metrics are not used frequently in SPS modeling while resource external metrics are widely used. We analyze the causal relationships between metrics. We find that the models exhibit significant diversity, as no pairwise relationship between metrics accounts for more than 10% SPS models. We identify 17 data issues may encounter in measurement and 10 coping strategies. The results of this study provide process modelers with an evidence‐based reference of the identification and the use of metrics in SPS modeling and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice.

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Evaluation of significance of control directives for software errors detection in functional programming of on-board computers

Drabov

Романова

Novikova

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Modern farm machinery is equipped with on-board computers that execute complicated tasks. The main efforts are aimed at selecting and justifying methods for adequate hardware-software translation of functional programming from one computer system to another, while increasing its stability to failures of computer hardware. The development of approaches to the selection of procedures for detecting errors determines the degree of their relative information value when analyzing the programs progress. As it follows from the analysis of error types in functional programming, it is established that errors occur at the stages of forming the technical task, when developing algorithms, when programming, and when configuring programs. Errors have different frequency and different weight in terms of the impact on the functioning of on-board computers used in agricultural machinery. The article contains a detailed list of errors that must be detected when debugging working programs of on-board computers. Based on expert assessments, the errors are divided into 33 different types and formed into 6 groups. By combining errors, it is possible to minimize the time spent on performing static debugging of the functional programming of on-board computers. Using the hierarchy analysis method, matrices of paired comparisons of control directives with error types in each group are formed. Thus, the relationship between groups of errors, control directives, and values of error types is established. The first thing, programs debugging is performed using the control directives that have the highest degree of significance, since they can be used to identify and eliminate the most dangerous errors.

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Forecasting software development project characteristics using meta-modeling

Cited by 3 publications

References 4 publications

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes

Metrics for software process simulation modeling

Evaluation of significance of control directives for software errors detection in functional programming of on-board computers

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