發(fā)布時間：2018-07-09 21:57

  本文選題：移動App + 應(yīng)用商店�。� 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的興起和智能手機的流行,市場上已經(jīng)有數(shù)以百萬計的移動App可以安裝在用戶手機上并為其提供服務(wù)。用戶通過應(yīng)用商店下載、使用這些App,同時在應(yīng)用商店中以評論的方式對App的質(zhì)量進行反饋。用戶的評論對于App開發(fā)者至關(guān)重要。開發(fā)者對App進行更新時除了要考慮App本身的發(fā)展需求,更需要考慮用戶的需求與感受。將高質(zhì)量評論中所體現(xiàn)的信息融入到App更新中將有助于提高App的質(zhì)量和評級。然而目前為止,尚未有明確研究表明開發(fā)者是否利用、以何種程度利用用戶評論信息對自身App進行改進,從而提升服務(wù)質(zhì)量。針對以上問題,本文通過提取用戶評論和App更新日志中的特征并識別它們之間的潛在關(guān)系,利用對原子更新單元(Atomic Update Unit,abbr.AU)進行聚類的辦法發(fā)現(xiàn)了7種更新模式(Update Pattern,abbr.UP)。更新模式體現(xiàn)了開發(fā)者以何種強度在何種及時程度以及充分程度上對用戶請求進行響應(yīng)的一種共性行為模式。同時,針對更新模式進行了一系列的實證研究。本文的結(jié)論幫助開發(fā)人員清楚地了解到自身對用戶評論進行反饋的習(xí)慣,對開發(fā)人員如何充分利用用戶評論提升App質(zhì)量提供了建議。具體研究內(nèi)容包括以下幾個部分:(1)實現(xiàn)了一種針對Google Play應(yīng)用商店上App的更新日志和評論的自動化收集工具。詳細介紹了工具在云端的部署和維護的過程。定義了待收集App數(shù)據(jù)的數(shù)據(jù)模型。對收集到的數(shù)據(jù)進行了統(tǒng)計層面的分析并得到了一些統(tǒng)計結(jié)果。(2)定義了原子更新單元(Atomic Update Unit,abbr.AU)并介紹了其生成方法,給出了針對原子更新單元的及時性、充分性、特征更新強度、用戶特征請求強度變化趨勢的計算方法。設(shè)計了一種針對用戶特征請求強度/特征更新強度變化趨勢(Intensity Trend Chart for Feature Request and Update,abbr.TC)的分段擬合歸一化算法。(3)定義了更新模式(Update Pattern,abbr.UP)。給出了挖掘更新模式的方法,挖掘得到了七種更新模式并對其進行分析。(4)進行了一系列實證研究,得到了一些結(jié)論:App開發(fā)者對某特征進行更新時采用哪種模式很大程度上取決于開發(fā)者本身喜好而不是該特征的本質(zhì);開發(fā)者采用更新的穩(wěn)定性存在明顯分化,約有65%的App的的更新穩(wěn)定性處于較較不穩(wěn)定狀態(tài),與此同時,有12%的App的更新穩(wěn)定性處于非常穩(wěn)定狀態(tài),這部分開發(fā)者更傾向于對自身App的同一特征在歷史更新中采用相同的模式;發(fā)現(xiàn)了兩種模式與App評論數(shù)量有顯著正相關(guān),發(fā)現(xiàn)三種更新模式與App在應(yīng)用商店中的排名有負相關(guān)。發(fā)現(xiàn)了一種更新模式與App的評分有正相關(guān)。
[Abstract]:With the rise of the Internet and the popularity of smartphones, there are already millions of mobile App on the market can be installed on user phones and provide services. Users download it from the app store, use it, and comment on the quality of App in the app store. User comments are critical to App developers. Developers need to consider not only the development needs of App itself, but also the needs and feelings of users when updating App. Incorporating the information embodied in high-quality reviews into App updates will help improve the quality and rating of App. However, up to now, there is no clear research on whether or not developers use the user comment information to improve their App, so as to improve the quality of service. In order to solve the above problems, by extracting the features from user comments and App update logs and recognizing the potential relationships between them, seven update patterns (update abbr.up) are found by clustering Atomic Update unit abbr.AU. The update pattern reflects a common behavior pattern in which the developer responds to the user's request in what degree of timeliness and adequacy. At the same time, a series of empirical studies on the renewal model are carried out. The conclusion of this paper helps developers to understand their habit of feedback on user reviews and provides suggestions on how to make full use of user reviews to improve the quality of App. The main contents of this paper are as follows: (1) an automatic collection tool for App update logs and comments in the Google play App Store is implemented. The deployment and maintenance of the tool in the cloud are introduced in detail. The data model of App data to be collected is defined. The collected data are analyzed at the statistical level and some statistical results are obtained. (2) the Atomic Update unit (AU) is defined and its generating method is introduced, and the timeliness, adequacy and characteristic update intensity of the atomic update unit are given. The calculation method of intensity change trend of user feature request. A segmented fitting normalization algorithm for intensity trend Chart for feature request and Updateabbr.TC is designed. (3) Update pattern is defined. In this paper, the method of mining update patterns is given, and seven updating patterns are obtained and analyzed. (4) A series of empirical studies are carried out. Some conclusions are drawn: the pattern used by App developers to update a feature depends to a large extent on the nature of the feature rather than the nature of the feature; the stability of the developer's adoption of the update is clearly divided. About 65% of the App have relatively unstable renewal stability, while 12% of the App are in a very stable state. This part of developers tend to use the same pattern for the same characteristics of their own App in the historical update, and found that the two models have significant positive correlation with the number of App reviews, and the three update patterns have negative correlation with the ranking of App in the application store. A positive correlation was found between an update model and the App score.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP311.56

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