logseq/pages/TOSEM-2025-0054.md

type:: [[REVIEWS]]
tags:: 
year:: 2025
venue:: [[TOSEM]] 
full-title::  Atten-TPL: A Novel TPL Recommendation Model Based on Attention Mechanism
date-start:: [[02-04-2025]] - 15:07
date-submitted::
external-links::
status:: [[DONE]] 
deadline-submission:: 
file:: [[@TOSEM-2025-0054_Proof_hi]] 
parent::
todoist:: https://app.todoist.com/app/task/tosem-2025-0054-reviewer-agreed-6XCQHqwm45x466c8

	- ### [[Highlights]]
	  collapsed:: true
		- # Annotazioni  
		  
		  (1/4/2025, 14:25:59)
		- “Software Engineering and Methodology” ([“TOSEM-2025-0054_Proof_hi”, p. 1](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=2&annotation=MUME8VC4)) #ffd400
		- “Automated Software Engineering” ([“TOSEM-2025-0054_Proof_hi”, p. 1](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=2&annotation=DZT6R83L)) #ffd400
		- “Transactions on Software Engineering” ([“TOSEM-2025-0054_Proof_hi”, p. 1](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=2&annotation=D74ABDAT)) #ffd400
		- “we did not compare our method with the state-of-the-art TPL recommendation method” ([“TOSEM-2025-0054_Proof_hi”, p. 1](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=2&annotation=IDYBZMRM)) #5fb236
		- “we did not evaluate the performance of our method in” ([“TOSEM-2025-0054_Proof_hi”, p. 1](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=2&annotation=CY9TGICR)) #5fb236
		- “function information of the app itself.” ([“TOSEM-2025-0054_Proof_hi”, p. 2](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=3&annotation=FI4CUIXS)) #a28ae5
		- “each TPL in the TPL context equally” ([“TOSEM-2025-0054_Proof_hi”, p. 2](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=3&annotation=WEBEHD8A)) #a28ae5
		- “ining the hidden relationships among candidate TPL” ([“TOSEM-2025-0054_Proof_hi”, p. 2](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=3&annotation=4CQQ93CJ)) #a28ae5
		- “the TPL context,” ([“TOSEM-2025-0054_Proof_hi”, p. 2](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=3&annotation=39MTDWH2)) #a28ae5
		- “LLM to mine app functions from description texts.” ([“TOSEM-2025-0054_Proof_hi”, p. 2](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=3&annotation=CAJ6K9NT)) #a28ae5
		- “Finding the synergy between different TPLs is another time-consuming process for developer” ([“TOSEM-2025-0054_Proof_hi”, p. 3](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=4&annotation=96TR2FYA)) #5fb236
		- “CF-based recommendation methods are often susceptible to the popularity bias [14, 28], that is, a small fraction of popular TPLs - those that are used by a large number of apps - dominate the prediction results and most other TPLs are ill-served.” ([“TOSEM-2025-0054_Proof_hi”, p. 3](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=4&annotation=66VTZ5VG)) #5fb236
		- “mogenization of node” ([“TOSEM-2025-0054_Proof_hi”, p. 3](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=4&annotation=AUTL8HFN)) #a28ae5
		- “ignore the function information of the app” ([“TOSEM-2025-0054_Proof_hi”, p. 3](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=4&annotation=QTP8APSD)) #a28ae5
		- “value that reflects the match degree between app product and candidate TPL.” ([“TOSEM-2025-0054_Proof_hi”, p. 3](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=4&annotation=IBW2X4QD)) #5fb236
		- “Fig. 1. TPL usage for two different apps” ([“TOSEM-2025-0054_Proof_hi”, p. 4](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=5&annotation=5QM5CCCW)) #5fb236
		- “we mine their description texts.” ([“TOSEM-2025-0054_Proof_hi”, p. 4](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=5&annotation=E2LPWP83)) #a28ae5
		- “app product” ([“TOSEM-2025-0054_Proof_hi”, p. 4](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=5&annotation=JBSFZLBY)) #5fb236
		- “candidate TPL.” ([“TOSEM-2025-0054_Proof_hi”, p. 4](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=5&annotation=DB223GRS)) #5fb236
		- “function information of app products” ([“TOSEM-2025-0054_Proof_hi”, p. 4](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=5&annotation=2H9EUL6N)) #5fb236
		- “reliability and diversity” ([“TOSEM-2025-0054_Proof_hi”, p. 4](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=5&annotation=NVHZ4HF2)) #5fb236
		- “Meanwhile, since T3 and T5 are as relevant to the development of video game as T2, we should pay more attention to T3 and T5 among T1, T3, and T5 to explore whether T2 can synergize with them to complete some video game-related development tasks.” ([“TOSEM-2025-0054_Proof_hi”, p. 5](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=6&annotation=577Q5AUV)) #a28ae5
		- “App. App refers to mobile application.” ([“TOSEM-2025-0054_Proof_hi”, p. 6](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=7&annotation=UGMBQQ4F)) #ffd400  
		  
		  *Is the proposed approach only for supporting the development of mobile applications?*
		- “by app product.” ([“TOSEM-2025-0054_Proof_hi”, p. 6](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=7&annotation=Y3YDXCUM)) #ff6666  
		  
		  *which one?*
		- “TPLs in the historical data.” ([“TOSEM-2025-0054_Proof_hi”, p. 6](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=7&annotation=BS4PA2QK)) #ffd400  
		  
		  *what do you mean?*
		- “apps in the historical data” ([“TOSEM-2025-0054_Proof_hi”, p. 6](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=7&annotation=YHTU3BXB)) #ffd400  
		  
		  *What do you mean?*
		- “we first mine the functions from its description text by utilizing LLM” ([“TOSEM-2025-0054_Proof_hi”, p. 6](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=7&annotation=CUZNAPHJ)) #e56eee
		- “app product and each candidate TPL in the TPL pooling, and thereby establishing a TPL recommendation list.” ([“TOSEM-2025-0054_Proof_hi”, p. 6](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=7&annotation=T2IMPN85)) #ffd400  
		  
		  *This is a potential and initial list, isn't it?*
		- “pp product and candidate TPL” ([“TOSEM-2025-0054_Proof_hi”, p. 7](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=8&annotation=CCBNZEWP)) #ffd400  
		  
		  *only one candidate TPL is given? Or more than one recommended items are given?*
		- “provided” ([“TOSEM-2025-0054_Proof_hi”, p. 7](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=8&annotation=LH8CW4GP)) #ffd400  
		  
		  *to be provided, isn't it?*
		- “To provide better support for TPL recommendation, we employ LLM to mine the functions from the description text of app product.” ([“TOSEM-2025-0054_Proof_hi”, p. 7](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=8&annotation=U6AR2GWV)) #ffd400  
		  
		  *it's uncommon to have such a text before the release of the wanted app.*
		- “generating” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=D2XJGWEW)) #ffd400  
		  
		  *generating a list of app functions can be a threat to validity. Check if it is stated later in the paper. I'm expecting a research question on this point.*
		- “First, Atten-TPL generates the m-dimensional vectors for candidate TPL and TPLs in the TPL context using One-hot Encoding [31], where m is the number of TPLs in the TPL pooling. Second, Atten-TPL multiplies the vector of candidate TPL and the ones of TPLs in the TPL context with two trainable weight matrixes W q ∈ Rm×dk and W k ∈ Rm×dk to obtain Q ∈ R1×dk and K ∈ Rn×dk , respectively (n is the number of TPLs in the TPL context).” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=9ETZLN9Q)) #ffd400  
		  
		  *A concrete example is necessary here. An illustrative project with the corresponding TPL and the corresponding represenation (even abstract) to help reader understand how the proposed encoding works.*
		- “Task” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=BEQ7MMJB)) #2ea8e5
		- “Function definition” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=GWSI8Y27)) #2ea8e5
		- “Function examples” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=HSDHJPKG)) #2ea8e5
		- “we provide five function examples, which are manually extracted from the description texts of existing app products” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=I8VCQTJ3)) #5fb236
		- “Non-functional content” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=N9I9BAK4)) #2ea8e5
		- “Input and output” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=4IEI6EAD)) #2ea8e5
		- “Atten-TPL” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=PNFEZLKW)) #e56eee
		- “each candidate TPL” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=IF67NTWC)) #5fb236
		- “Generating the vector for TPL context.” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=LNNR6AGP)) #2ea8e5
		- “app product” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=FJDZHARG)) #5fb236
		- “not every TPL in the TPL context can make a positive contribution.” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=IQ7LHE4P)) #a28ae5
		- “truly useful for the task” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=YYH7Q8SH)) #e56eee
		- “m-dimensional vectors” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=CD43J6KY)) #5fb236
		- “formed randomly from the vectors of all TPLs in the TPL context.” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=Z3LDN75V)) #ffd400  
		  
		  *This is not clear!*
		- “After that, the model applies a softmax operation to normalize all attention scores so that they are added up to 1.” ([“TOSEM-2025-0054_Proof_hi”, p. 8](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=9&annotation=LEGYZN8Y)) #5fb236
		- “match degree between app product and candidate TPL.” ([“TOSEM-2025-0054_Proof_hi”, p. 9](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=10&annotation=HR2KJSKU)) #a28ae5
		- “(3)” ([“TOSEM-2025-0054_Proof_hi”, p. 9](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=10&annotation=95HS25FS)) #5fb236
		- “Evaluating the match degree between app prod” ([“TOSEM-2025-0054_Proof_hi”, p. 9](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=10&annotation=DYKFEPQC)) #2ea8e5
		- “activations, bias, and model weights” ([“TOSEM-2025-0054_Proof_hi”, p. 9](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=10&annotation=L5K5L6VJ)) #e56eee
		- “sentence-level tasks” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=N664MLML)) #5fb236
		- “We create a dataset consisting of positive triples and negative triples based on the hold-one-out strategy [7], and each triple contains three parts, including: candidate TPL, TPL context, and function list.” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=3LMR73ZH)) #ffd400  
		  
		  *The creation of this dataset is also a critical point of the approach, which can represent a threat to validity.*
		- “The function list is obtained by utilizing LLM to process the description text of app product.” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=I4KD3I76)) #ffd400  
		  
		  *As I said the fact that the function lists are obtained by means of LLMs represents a crucial threat to validity because creating a clean and correct dataset is of paramount importance in this case.*
		- “Meanwhile, we also establish multiple negative triples for each app, and the number of negative triples is ten times the number of positive triples” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=IRWFGEQ5)) #5fb236
		- “softmax classifier” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=6QHCKG87)) #a28ae5
		- “Second, Atten-TPL calculates the mean for the vectors of all functions and uses the result as the vector of function list.” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=C32MKF3L)) #ffd400  
		  
		  *Also here, can you give an illustrative example?*
		- “hold-one-out strategy” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=8KTUDYDG)) #a28ae5
		- “each triple contains three parts, i” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=5U26GS48)) #a28ae5
		- “In each operation, we remove a TPL from the TPL list of app product, the removed TPL is treated as candidate TPL, and the remaining TPLs are used as the TPL context. The function list is obtained by utilizing LLM to process the description text of app product” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=WZM2CELA)) #ffd400  
		  
		  *how many times has this been done? For how many product, how many candidate TPLs?*
		- “we remove a TPL from the TPL list of app product” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=HRKDKIJZ)) #a28ae5
		- “Positive triples and negative triples are labeled 1 and 0, respectively.” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=Z2LSEHRR)) #a28ae5
		- “How does Atten-TPL perform on the TPL recommendation?” ([“TOSEM-2025-0054_Proof_hi”, p. 10](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=11&annotation=MT7F4E75)) #2ea8e5
		- “function information of app product actually improve the performance of Atten-TPL?” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=E8CFN8PW)) #2ea8e5
		- “attention mechanism positively affect” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=EABZ54WA)) #2ea8e5
		- “Can Atten-TPL provide help for app developers in software engineering practice?” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=MGQTAR67)) #ffd400  
		  
		  *This is a qualitative research question. Are used involved?Let's see what the authors have done for this.*
		- “MALib dataset [14], a public real-world dataset that contains 61,722 apps, 827 distinct TPLs, and 725,502 app-TPL usage records.” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=SZ4RRBWN)) #a28ae5
		- “Since the MALib dataset does not provide the description texts needed for our recommendation model, we wrote a web crawler to scrape the description texts for apps in it from app store or third-party website.” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=HLRVCBJN)) #ffd400  
		  
		  *This can be a potential threat to validity. Have you double checked the crawled data and checked that they are linked to the projects correctly?*
		- “44,873 apps, and these apps were used as the dataset of our experiment.” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=GDVYVMGB)) #ffd400  
		  
		  *Have you double checked that the crawled information is consistent with the corresponding apps?*
		- “TPL author, TPL Dependency” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=FJVGFINR)) #5fb236
		- “0.2,” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=HMRTA5TP)) #5fb236
		- “The larger the value, the more random the generated text, while the smaller the value, the less random the generated text, resulting in the more organized and precise response.” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=ILN5QJH5)) #5fb236
		- “2rm” ([“TOSEM-2025-0054_Proof_hi”, p. 11](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=12&annotation=Y8CVHA8L)) #ffd400  
		  
		  *what is it?*
		- “Atten-TPL mines app functions from the description text by employing ChatGPT” ([“TOSEM-2025-0054_Proof_hi”, p. 15](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=16&annotation=ZPCZ95Z5)) #ffd400
		- “n addition to the TPL context, the input of our recommendation method includes the description text of app product. Considering that the description text must be completed by app developers before the application is” ([“TOSEM-2025-0054_Proof_hi”, p. 19](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=20&annotation=TZ5UAM57)) #5fb236
		- “In this paper, we propose a novel TPL recommendation model called Atten-TPL. Atten-TPL can analyze the match degree between app product and each candidate TPL in the TPL pooling by utilizing a deep neural network to mine the hidden relationships among candidate TPL, the TPL context and the functions of app product. By employing the attention mechanism, Atten-TPL can pay more attention to TPLs in the TPL context that are truly useful for the evaluation task. Furthermore, to effectively gain the functions provided by apps, we use LLM to mine their description texts. Compared to existing state-of-the-art TPL recommendation methods, Atten-TPL can return more reliable recomemndation results and performs well in terms of diversity. In future work, we plan to propose a method to generate the explanatory texts for TPL recommendation lists, which will assist developers in understanding and utilizing the recommended TPLs. The robust text generation capability of LLM holds promise for achieving this goal.” ([“TOSEM-2025-0054_Proof_hi”, p. 20](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=21&annotation=MW9Q7GPE)) #a28ae5
		- “released, our recommendation model is highly practical in real-world development.” ([“TOSEM-2025-0054_Proof_hi”, p. 20](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=21&annotation=T29QQIRD)) #5fb236
		- “Meanwhile, even if the developers of app product do not provide the description text, our model can still rely on the TPL context to recommend the available TPLs for them with acceptable results (The experimental result of RQ2 can demonstrate this). Furthermore, since the TPL ecosystem is dynamic, the app and TPL poolings can be updated and the recommendation model can be retrained periodically, enabling the model to integrate new or updated TPLs.” ([“TOSEM-2025-0054_Proof_hi”, p. 20](zotero://select/library/items/9SGE9R47)) ([pdf](zotero://open-pdf/library/items/RNTJP3AD?page=21&annotation=8AY77D32)) #ffd400
	- ### [[Comments]]
		- **SUMMARY:** The paper presents Atten-TPL, a recommender system based on the attention mechanism to suggest Third-Party Libraries to developers. The approach extracts functional information from app descriptions and analyzes relationships between candidate TPLs, TPL context, and the extracted app functionalities. The approach leverages ChatGPT to mine functional descriptions from app description texts. The approach has been evaluated using and extending the MALib dataset, outperforming existing baselines.
		- **COMMENTS:** The paper is about a relevant problem. However, I have some concerns that are related to the following issues, which require a revision of the work:
			- The reliance on ChatGPT to generate app functions from description texts is a critical threat to validity. It is essential to explicitly address this issue by clearly discussing potential inaccuracies or biases introduced during the dataset creation process and how they might impact the results.
			- The process of data scraping from external app stores to complement the MALib dataset introduces another threat to validity. Clarification is needed on how the crawled data were validated to ensure they correctly corresponded with the respective app records.
			- The description texts are claimed to be practical because they must be completed before app release. This is an important issue of the proposed approach, which requires a proper description of the application before its actual development and release. Authors should at least consider this aspect and discuss its implications on adopting the approach in practice.
			- Authors should clarify whether the method exclusively targets mobile applications or if it has broader applicability.
			- Definitions of key terms such as "TPLs in historical data" and "apps in historical data" need more explicit clarification to improve readability (see section 2.2).
			- The explanation "formed randomly from the vectors of all TPLs in the TPL context" on page 7 is unclear and requires further elaboration.
			- Illustrative examples are needed to improve the description of critical processes such as vector encoding and dataset creation (e.g., concrete scenarios demonstrating the Atten-TPL method with sample data).
			- On page 5 (section 3), clarification is needed on whether the recommended TPL list provided initially is a preliminary or final recommendation.
			- On page 6 (just before section 3.1), the text suggests ambiguity on whether the recommendation involves a single candidate TPL or multiple candidates. Clarification is necessary.
			- On page 9, the "hold-one-out strategy" implementation needs further detail, specifically regarding the scale of experiments (number of products, candidate TPLs, and repetitions).
			- Page 6: "function provided by app"—likely intended as "to be provided."
			- Regarding the baselines considered, it is essential to include a qualitative discussion about (at least) CrossRec and LibSeek, which exhibit notably poor performance. This contrasts with the original papers, where these methods outperformed existing baselines at the time of publication. The significant difference in accuracy raises concerns about possible biases, either related to the datasets employed by the original authors or those created specifically for this study.
		- To summarize, I like the paper however I suggest authors discuss threats to validity particularly regarding the data quality from LLM-generated descriptions and web-crawled app descriptions. Moreover, it is necessary to include illustrative examples to clarify complex technical steps (e.g., vector representation and dataset creation).