Name
Title | ||
|---|---|---|
Weka meets TraceLab: Toward convenient classification: Machine learning for requirements engineering problems: A position paper |
Abstract | ||
|---|---|---|
Requirements engineering encompasses many difficult, overarching problems inherent to its subareas of process, elicitation, specification, analysis, and validation. Requirements engineering researchers seek innovative, effective means of addressing these problems. One powerful tool that can be added to the researcher toolkit is that of machine learning. Some researchers have been experimenting with their own implementations of machine learning algorithms or with those available as part of the Weka machine learning software suite. There are some shortcomings to using “one off” solutions. It is the position of the authors that many problems exist in requirements engineering that can be supported by Weka's machine learning algorithms, specifically by classification trees. Further, the authors posit that adoption will be boosted if machine learning is easy to use and is integrated into requirements research tools, such as TraceLab. Toward that end, an initial concept validation of a component in TraceLab is presented that applies the Weka classification trees. The component is demonstrated on two different requirements engineering problems. Finally, insights gained on using the TraceLab Weka component on these two problems are offered. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/AIRE.2014.6894850 | Artificial Intelligence for Requirements Engineering |
Keywords | Field | DocType |
decision trees,formal specification,learning (artificial intelligence),pattern classification,TraceLab Weka component,Weka classification trees,Weka machine learning software suite,one off solutions,requirements engineering problems,requirements research tools,Artificial intelligence,TraceLab,Weka,classification,decision trees,machine learning,requirements engineering | Decision tree,Computer science,Position paper,Requirements engineering,Supervised learning,Software,Classification tree analysis,Artificial intelligence,Statistical classification,Machine learning | Conference |
Citations | PageRank | References |
5 | 0.47 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jane Huffman Hayes | 1 | 5 | 0.47 |
| Wenbin Li | 2 | 5 | 0.47 |
| Mona Rahimi | 3 | 34 | 6.08 |